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Works482 from public data
- Deep Learning in Medical Ultrasound Analysis: A Review789
Several popular deep learning architectures are briefly introduced, and their applications in various specific tasks in US image analysis, such as classification, detection, and segmentation are discussed.
- FAT-Net: Feature adaptive transformers for automated skin lesion segmentation594
A novel feature adaptive transformer network based on the classical encoder-decoder architecture, named FAT-Net, which integrates an extra transformer branch to effectively capture long-range dependencies and global context information is proposed.
- Deep Learning Framework for Alzheimer’s Disease Diagnosis via 3D-CNN and FSBi-LSTM286
This work designs a novel deep learning framework and exploits the virtues of 3D-CNN and fully stacked bidirectional long short-term memory (FSBi-LSTM) to derive deep feature representation from both MRI and PET for AD diagnosis.
- Skin lesion segmentation via generative adversarial networks with dual discriminators279
The proposed GAN achieves superior segmentation performance to state-of-the-art methods and is evaluated on the public International Skin Imaging Collaboration (ISIC) Skin Lesion Challenge Datasets of 2017 and 2018.
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- Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review268
Although the DL methods show promising improvements in breast cancer diagnosis, there are still issues of data scarcity and computational cost, which have been overcome to a significant extent by applying data augmentation and improved computational power of DL algorithms.
- SCS-Net: A Scale and Context Sensitive Network for Retinal Vessel Segmentation259
The experimental results demonstrate the effectiveness of the proposed SCS-Net, which is capable of achieving better segmentation performance than other state-of-the-art approaches, especially for the challenging cases with large scale variations and complex context environments.
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- Blind and robust audio watermarking scheme based on SVD–DCT193
Experimental results show that the proposed watermarking method is comparable to, if not, better than SVD based method and several selected typical audioWatermarking methods, even in the presence of various common signal processing attacks.
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- A Robust Audio Watermarking Scheme Based on Lifting Wavelet Transform and Singular Value Decomposition161
Experimental and analysis results demonstrate that the proposed LWT-SVD method is not only robust against both general signal processing attacks and desynchronization attacks but also achieve a very good tradeoff between robustness, imperceptibility and payload.
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- Multi-center sparse learning and decision fusion for automatic COVID-19 diagnosis147
A joint multi-center sparse learning (MCSL) and decision fusion scheme exploiting chest CT images for automatic COVID-19 diagnosis and demonstrates that the proposed method can improve COVID-19 diagnosis performance and outperform the state-of-the-art methods.
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- Fully transformer network for skin lesion analysis141
This work proposes a Fully Transformer Network (FTN) to learn long-range contextual information for skin lesion analysis and shows that FTN consistently outperforms other state-of-the-art CNNs in terms of computational efficiency and the number of tunable parameters.
- Reversible watermarking scheme for medical image based on differential evolution141
Experimental results indicate that the proposed reversible watermarking method is not only highly competitive, but also outperforms the existing methods.
- Tensorizing GAN With High-Order Pooling for Alzheimer’s Disease Assessment139
The proposed Tensor-train, High-order pooling and Semisupervised learning-based GAN (THS-GAN) is the first work to deal with classification on MR images for AD diagnosis and achieves superior performance compared with existing methods.
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- Morphological Feature Visualization of Alzheimer’s Disease via Multidirectional Perception GAN132
By introducing a novel multidirectional mapping mechanism into the model, the proposed MP-GAN can capture the salient global features efficiently and clearly delineate the subtle lesions via MR image transformations between the source domain and the predefined target domain.
- Graph convolution network with similarity awareness and adaptive calibration for disease-induced deterioration prediction129
This paper proposes three mechanisms to improve GCN, namely similarity-aware adaptive calibrated GCN (SAC-GCN), for predicting SMC and MCI, and devise a calibration mechanism to fuse functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) information into edges.
- Multi-task learning for quality assessment of fetal head ultrasound images128
A new multi-task learning framework using a faster regional convolutional neural network (MF R-CNN) architecture for standard plane detection and quality assessment and achieves promising performance compared with state-of-the-art methods.
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- Artificial intelligence and multimodal data fusion for smart healthcare: topic modeling and bibliometrics118
A bibliometric and topic modeling examination on 683 articles from 2002 to 2022, focusing on research topics and trends, journals, countries/regions, institutions, authors, and scientific collaborations showed that the number of articles has grown from 1 in 2002 to 220 in 2022.
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- Driver fatigue transition prediction in highly automated driving using physiological features117
This research aims to predict the driver's transition from non-fatigue to fatigue in highly automated driving using physiological features and capitalized on PERCLOS as the ground truth of driver fatigue to select the most important physiological features to predict driver fatigue proactively.
- A deeply supervised residual network for HEp-2 cell classification via cross-modal transfer learning111
Extensive experiments show that the proposed framework based on very deep supervised residual network (DSRN) to classify HEp-2 cell images delivers state-of-the-art performance and outperforms the traditional methods based on deep convolutional neural network (DCNN).
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- Deep and joint learning of longitudinal data for Alzheimer's disease prediction105
Extensive experiments on the public database of Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrate that the proposed framework can effectively reveal the relationship between clinical score and MRI data and outperforms the state-of-the-art methods in scores prediction.
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- GP-GAN: Brain tumor growth prediction using stacked 3D generative adversarial networks from longitudinal MR Images95
A novel data-driven method via stacked 3D generative adversarial networks (GANs), named GP-GAN, that outperforms state-of-the-art methods for glioma growth prediction and attain average Jaccard index and Dice coefficient of 78.97% and 88.26%, respectively.
- Self-calibrated brain network estimation and joint non-convex multi-task learning for identification of early Alzheimer's disease92
A new model to automatically diagnosing MCI and its earlier stages is proposed by combining low-rank self-calibrated functional brain networks and structural brain networks for joint multi-task learning and achieves promising performance with high classification accuracy.
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- Dual attention enhancement feature fusion network for segmentation and quantitative analysis of paediatric echocardiography85
A dual-path feature extraction module (DP-FEM) to extract rich features via a channel attention mechanism and a high- and low-level feature fusion module (HL-FFM) is devised based on spatial attention, which selectively fuses rich semantic information from high-level features with spatial cues from low- level features.
- Breast Cancer Image Classification via Multi-Network Features and Dual-Network Orthogonal Low-Rank Learning84
A classification framework based on histology images by combining deep learning with machine learning methodologies is devised and can achieve quite promising performance and outperform state-of-the-art methods.
- ADAM Challenge: Detecting Age-Related Macular Degeneration From Fundus Images83
The ensembling strategy and the incorporation of clinical domain knowledge were the key to improve the performance of the deep learning models in the Automatic Detection challenge on Age-related Macular degeneration (ADAM).
- Discriminative Learning for Alzheimer's Disease Diagnosis via Canonical Correlation Analysis and Multimodal Fusion81
A novel normalization method is devised to tackle the intra- and inter-subject variations from the multimodal data, which outperforms the state-of-the-art methods in the literature.
- A New Brain Network Construction Paradigm for Brain Disorder via Diffusion-Based Graph Contrastive Learning79
The proposed DGCL can be served as a universal brain network construction scheme, which can effectively identify important brain connections through generative paradigms and has the potential to provide disease interpretability support for neuroscience research.
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- Brain stroke lesion segmentation using consistent perception generative adversarial network77
In semi-supervised segmentation task, the proposed CPGAN using only two-fifths of labeled samples outperforms some approaches using full labeled samples.
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- Segmentation of breast anatomy for automated whole breast ultrasound images with boundary regularized convolutional encoder–decoder network74
Based on the ADT and DBS techniques, the proposed ConvEDNet method achieves better segmentation performance compared with several classic deep learning segmentation methods on the same set of AWBUS images.
- Alzheimer’s disease diagnosis from multi-modal data via feature inductive learning and dual multilevel graph neural network73
This work proposes a novel framework for AD diagnosis that fuses gene, imaging, protein, and clinical data through a novel dual multilevel graph neural network (DMGNN) module, and consistently outperforms other state-of-the-art multi-modal fusion methods.
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- Alzheimer’s Disease Prediction via Brain Structural-Functional Deep Fusing Network70
A novel model termed cross-modal transformer generative adversarial network (CT-GAN) is proposed to effectively fuse the functional and structural information contained in functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI).
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- Self-co-attention neural network for anatomy segmentation in whole breast ultrasound70
A new deep learning encoder-decoder segmentation method based on a self-co-attention mechanism that corroborate the effectiveness of the proposed method on the difficult breast anatomy segmentation problem on AWBUS images.
- Brain MR to PET Synthesis via Bidirectional Generative Adversarial Network65
A novel end-to-end network, called Bidirectional GAN, where image contexts and latent vector are effectively used and jointly optimized for brain MR- to-PET synthesis, and a bidirectional mapping mechanism is designed to embed the diverse brain structural details into the high-dimensional latent space.
- Augmenting feature model through customer preference mining by hybrid sentiment analysis63
This paper uses sentiment analysis of online product reviewers to extract customer preference information and is able to predict sentence sentiments for individual product features with acceptable accuracy, and thus augment a feature model by integrating positive and negative opinions of the customers.
- High-intensity focused ultrasound (HIFU) ablation versus surgical interventions for the treatment of symptomatic uterine fibroids: a meta-analysis62
HIFU ablation is superior to surgery in terms of symptomatic relief, improvement in QoL, recovery, and significant complications, and HIFU showed comparable effects to surgery regarding the incidence of adverse events, symptom recurrence, re-intervention, and pregnancy.
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- Multi-scale Enhanced Graph Convolutional Network for Early Mild Cognitive Impairment Detection61
A novel framework based on multi-scale enhanced GCN (MSE-GCN) is designed in this paper, which fuses the functional and structural information from the resting-state functional magnetic resonance imaging and diffusion tensor imaging, respectively to detect EMCI.
- Adaptive sparse learning using multi-template for neurodegenerative disease diagnosis59
An adaptive feature learning framework using multiple templates for early diagnosis based on multiple brain parcellation atlases with various regions of interest demonstrates that accurate feature learning facilitates the identification of the highly relevant brain regions with significant contribution in the prediction of disease progression.
- Characterization Multimodal Connectivity of Brain Network by Hypergraph GAN for Alzheimer’s Disease Analysis58
A novel Hypergraph Generative Adversarial Networks (HGGAN) is proposed in this paper, which utilizes Interactive Hyperedge Neurons module and Optimal Hypergraph Homomorphism algorithm to generate multimodal connectivity of Brain Network from rs-fMRI combination with DTI.
- Multimodal Representations Learning and Adversarial Hypergraph Fusion for Early Alzheimer’s Disease Prediction57
A novel multimodal representation learning and adversarial hypergraph fusion (MRL-AHF) framework for Alzheimer's disease diagnosis using complete trimodal images and provides a possible way to understand the underlying mechanisms of disorder's progression by analyzing the abnormal brain connections.
- Diagnosis of early Alzheimer’s disease based on dynamic high order networks56
A novel framework to exploit the high-level dynamic interactions among brain regions for early AD diagnosis is developed, and the proposed approach achieves promising results for AD classification, but also successfully recognizes disease-related biomarkers.
- Image-guided thermal ablation in the management of symptomatic adenomyosis: a systematic review and meta-analysis54
Compared with those at baseline, the visual analog scale scores, the symptom severity scores and the menorrhagia severity scores decreased significantly after these thermal ablation therapies for symptomatic adenomyosis.
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- Fine-grained facial expression analysis using dimensional emotion model53
This paper proposes to train deep convolutional neural networks (CNNs) to analyze facial expressions explainable in a dimensional emotion model and improves the method by a bilinear pooling which encodes second-order statistics of features.
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- Morphology-based deep learning enables accurate detection of senescence in mesenchymal stem cell cultures52
A successful morphology-based Cascade region-based convolution neural network system for detecting senescent MSCs, which can automatically locate single cells of different sizes and shapes in multicellular images and assess their senescence state, showing promising performance in both chronic and acute MSC senescence.
- Medical image fusion method based on dense block and deep convolutional generative adversarial network52
A novel medical image fusion algorithm based on deep convolutional generative adversarial network and dense block models, which is used to generate fusion images with rich information is proposed, which has excellent detail preserve characteristics and can suppress the artificial effects.
- Brain Structure-Function Fusing Representation Learning Using Adversarial Decomposed-VAE for Analyzing MCI51
A novel brain structure-function fusing-representation learning (BSFL) model is proposed to effectively learn fused representation from diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI) for mild cognitive impairment (MCI) analysis.
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- Convolutional descriptors aggregation via cross-net for skin lesion recognition49
Comparing with aggregation strategy using pooling approaches, the proposed method learns more robust and discriminative representations based on the publicly available skin lesion challenge datasets from the International Symposium on Biomedical Imaging (ISBI) 2016 and 2017.
- Self-supervised multi-modal fusion network for multi-modal thyroid ultrasound image diagnosis48
A deep learning-based multi-modal feature fusion network for the automatic diagnosis of thyroid disease based on the ultrasound images is proposed and verified, proving that this method could provide fast and accurate assistance for sonographers in diagnosing thyroid nodules.
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- Content-based classification of breath sound with enhanced features48
The experimental results demonstrate that the high accuracy of the pathological BS data can provide reliable diagnostic suggestions for breath disorders, such as flu, pneumonia and bronchitis and validate the performance of the proposed feature sets and classification model.
- Self-weighted adaptive structure learning for ASD diagnosis via multi-template multi-center representation47
A novel multi- template multi-center ensemble classification scheme for automatic ASD diagnosis based on different pre-defined templates is proposed, which boosts ASD diagnosis performance and outperforms state-of-the-art methods.
- Joint detection and clinical score prediction in Parkinson's disease via multi-modal sparse learning47
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- CAT-DTI: cross-attention and Transformer network with domain adaptation for drug-target interaction prediction46
CAT-DTI, a model based on cross-attention and Transformer, possessing domain adaptation capability, is proposed, which improves DTI prediction performance compared with previous methods.
- Multi-scale wavelet network algorithm for pediatric echocardiographic segmentation via hierarchical feature guided fusion46
A multi-scale wavelet network combined with a bidirectional feature fusion (BFF-Net) and a wavelet-Unet (W- unet) for end-to-end pediatric echocardiographic segmentation is proposed and verified its potential and effectiveness as a clinical auxiliary tool.
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- High tissue contrast image synthesis via multistage attention-GAN: Application to segmenting brain MR scans43
This study shows the potential advantages of synthetic high tissue contrast (HTC) images through image-to-image translation techniques and uses a novel cycle generative adversarial network (Cycle-GAN), which provides a mechanism of attention to increase the contrast within the tissue.
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- Automatic Recognition of Fetal Facial Standard Plane in Ultrasound Image via Fisher Vector43
A new algorithm is developed for the automatic recognition of the fetal facial standard planes such as the axial, coronal, and sagittal planes that is implemented by support vector machine (SVM) classifier based on the stochastic dual coordinate ascent (SDCA) algorithm.
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- Brain Tumor Synthetic Segmentation in 3D Multimodal MRI Scans42
It is shown that a convolutional neural network trained on high-contrast images can transform intensity distribution of brain lesion in its internal subregions and generative adversarial network (GAN) is extended to synthesize high-Contrast images.
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- Multipurpose watermarking scheme via intelligent method and chaotic map42
Experimental results demonstrate that the proposed scheme achieves promising performance in both copyright protection and content authentication simultaneously, which confirm the superiority of the proposed algorithm as compared to existing methods.
- Optimal and secure audio watermarking scheme based on self-adaptive particle swarm optimization and quaternion wavelet transform42
The experimental results validate that the SAPSO scheme is not only robust against de-synchronization attack, but also typical signal manipulations and StirMark attack, and the comparative analysis revealed that the proposed scheme outperforms the state-of-the-arts audio watermarking schemes.
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- Cross-attention multi-branch network for fundus diseases classification using SLO images41
A novel deep learning method to complete different fundus diseases classification tasks using ultra-wide field scanning laser ophthalmoscopy (SLO) images, which have an ultra- wide field view of 180-200˚, and can outperform the state-of-the-art methods.
- Modality Completion via Gaussian Process Prior Variational Autoencoders for Multi-modal Glioma Segmentation40
A novel model, Multi-modal Gaussian Process Prior Variational Autoencoder (MGP-VAE), to impute one or more missing sub-modalities for a patient scan is proposed and shown to be applicability on brain tumor segmentation where either, two, or three of four sub- modalities may be missing.
- Diff-UNet: A diffusion embedded network for robust 3D medical image segmentation39
Experimental results on the three latest large-scale datasets show that the first diffusion-based model with two branches with two branches for general 3D medical image segmentation, Diff-UNet quantitatively and qualitatively outperforms state-of-the-art 3D medical segmentation methods, especially on regions with small or complex structures.
- Aggregating Deep Convolutional Features for Melanoma Recognition in Dermoscopy Images38
The proposed framework for automated melanoma recognition in dermoscorpy images is simpler, more compact and capable of producing more discriminative features, outperforming state-of-the-art approaches in all evaluation metrics.
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- A Deep Learning Framework for Identifying Zone I in RetCam Images36
A new deep learning framework is developed to automatically identify zone I from RetCam images and results show that zone I identification accuracy of 91% is achieved when the IOU threshold is 0.8.
- Dense deconvolution net: Multi path fusion and dense deconvolution for high resolution skin lesion segmentation36
This work proposes a novel skin lesion segmentation network via a very deep dense deconvolution network based on dermoscopic images that shows the superiority over the state-of-the-art approaches based on the public available 2016 and 2017 skin lesions challenge dataset.
- AMD-GAN: Attention encoder and multi-branch structure based generative adversarial networks for fundus disease detection from scanning laser ophthalmoscopy images34
Experimental results demonstrate that the proposed generative adversarial network called AMD-GAN based on the attention encoder (AE) and multi-branch (MB) structure for fundus disease detection from SLO images is promising in detecting the diseased and normalSLO images with the experts labeling.
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- CATD: Unified Representation Learning for EEG-to-fMRI Cross-Modal Generation33
The proposed Condition-Aligned Temporal Diffusion framework establishes a new paradigm for cross-modal synthesis of neuroimaging by unifying heterogeneous neuroimaging data into a latent representation space, showing promise in medical applications such as improving Parkinson’s disease prediction and identifying abnormal brain regions.
- Ultrasound-guided Microwave Ablation in the Management of Symptomatic Uterine Myomas: A Systematic Review and Meta-analysis33
Ultrasound-guided MWA is an effective and safe minimally invasive therapy for symptomatic uterine fibroids and the risk of bias and evidence quality is evaluated.
- Bridging Computational Features Toward Multiple Semantic Features with Multi-task Regression: A Study of CT Pulmonary Nodules33
The experimental results suggest that the predicted semantic scores from MTR are closer to the radiologists’ rating than the predicted scores from single-task LASSO and elastic net regression methods.
- A linear threshold-hurdle model for product adoption prediction incorporating social network effects33
A linear threshold-hurdle model to predict product adoption incorporating social network effects is proposed and the notion of hurdle is introduced to capture the monetary aspect in users' decision making process of product adoption.
- Saliency-driven image classification method based on histogram mining and image score33
A new saliency-driven bag-of-phrase approach for image classification where saliency map and local features are first extracted from edge-based dense descriptors and represented by histogram and mined with discriminative learning technique.
- Optimal image watermarking scheme based on chaotic map and quaternion wavelet transform33
An intelligent watermarking scheme optimized by adaptive differential evolution (ADE) is proposed based on chaotic map and quaternion wavelet transform and demonstrates the high robustness against common signal processing operations, especially geometrical distortions of the proposed algorithm.
- IFT-Net: Interactive Fusion Transformer Network for Quantitative Analysis of Pediatric Echocardiography32
An interactive fusion transformer network (IFT-Net) is proposed for quantitative analysis of pediatric echocardiography, which achieves the bidirectional fusion between local features and global context information by constructing interactive learning between the convolution branch and the transformer branch.
- Automated left ventricular segmentation from cardiac magnetic resonance images via adversarial learning with multi-stage pose estimation network and co-discriminator31
An automated LV segmentation method based on adversarial learning by integrating a multi-stage pose estimation network (MSPN) and a co-discrimination network and outperforms the state-of-the-art methods in terms ofLV segmentation accuracy from the comparison results.
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- Federated learning via multi-attention guided UNet for thyroid nodule segmentation of ultrasound images30
A multi-attention guided UNet (MAUNet) for thyroid nodule segmentation is proposed, which uses a multi-scale cross attention module for initial image feature extraction and incorporates a dual attention module into the skip-connection step of the UNet network.
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- AIDAN: An Attention-Guided Dual-Path Network for Pediatric Echocardiography Segmentation30
A novel Cardiac Attention-guided Dual-path Network (AIDAN), which comprises a convolutional block attention module (CBAM) attached to a spatial and context paths, which can guide the network and learn the most discriminative features.
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- 3-D Brain Reconstruction by Hierarchical Shape-Perception Network From a Single Incomplete Image28
A novel hierarchical shape-perception network (HSPN) is proposed to reconstruct the 3-D point clouds of specific brains from one single incomplete image with low latency and outperforms other competitive methods in terms of qualitative displays, quantitative experiment, and classification evaluation.
- Unsupervised domain selective graph convolutional network for preoperative prediction of lymph node metastasis in gastric cancer28
A novel multi-source domain adaptation framework for LN metastasis diagnosis performance and outperforms state-of-the-art methods is proposed, which considers domain-invariant and domain-specific features, but also achieves the imbalanced knowledge transfer and class-aware feature alignment across domains.
- Automated detection of retinopathy of prematurity by deep attention network28
A deep convolutional neural network (DCNN) is developed for automated ROP detection using wide-angle retinal images and successfully detects the pathological structures of ROP in the retina images.
- 3D Convolutional Neural Network and Stacked Bidirectional Recurrent Neural Network for Alzheimer’s Disease Diagnosis28
This paper devise simple 3D-CNN architecture to obtain the deep feature representation from magnetic resonance imaging (MRI) and positron emission tomography (PET) images, respectively, and apply SBi-RNN on the local deep cascaded and flattened descriptors for performance boosting.
- Discriminative Learning for Automatic Staging of Placental Maturity via Multi-layer Fisher Vector28
A method to automatically stage placenta maturity from B-mode ultrasound (US) images based on dense sampling and novel feature descriptors and a multi-layer FV is investigated to exploit the spatial information rather than the single layer FV.
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- Auto-weighted centralised multi-task learning via integrating functional and structural connectivity for subjective cognitive decline diagnosis26
An auto-weighted centralised multi-task (AWCMT) learning framework for differential diagnosis of SCD and MCI is proposed and the newly proposed method has good interpretability as it is able to discover the most disease-related brain regions and their connectivity.
- Sparse feature learning for multi-class Parkinson’s disease classification26
Experimental results indicate that the proposed framework to construct a least square regression model based on the Fisher’s linear discriminant analysis (LDA) and locality preserving projection (LPP) outperforms state-of-the-art methods.
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- Osteoporosis Diagnostic Model Using a Multichannel Convolutional Neural Network Based on Quantitative Ultrasound Radiofrequency Signal25
It is indicated that the MCNN method may be more accurate than the conventional SOS method for osteoporosis diagnosis and the MCnn tool and ultrasound RF signal analysis are promising future developmental directions for QUS.
- Uterine Artery Embolization Compared with High-intensity Focused Ultrasound Ablation for the Treatment of Symptomatic Uterine Myomas: A Systematic Review and Meta-analysis25
Compared with HIFU ablation, UAE provided more significant alleviation of symptoms and improvement of QoL, lower postoperative re-intervention rate, and lower pregnancy rate for women with uterine fibroids, however, it cannot conclude that HifU was more favorable for the desired pregnancy than UAE because of the confounding factors.
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- Automated detection of nine infantile fundus diseases and conditions in retinal images using a deep learning system24
The IRIDS serves as an example of artificial intelligence integration into ophthalmology to achieve better outcomes in predictive, preventive, and personalized medicine (PPPM / 3PM) in the treatment of infantile fundus diseases.
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- High Tissue Contrast MRI Synthesis Using Multi-Stage Attention-GAN for Segmentation24
The experiments over three competitive segmentation baselines on BraTS 2018 dataset indicate that incorporating the synthetic HTC images in the multi-modal segmentation framework improves the average Dice scores 0.8%, 0.6%, and 0.5% on the whole tumor, tumor core, and enhancing tumor, respectively, while eliminating one real MRI sequence from the segmentation procedure.
- Longitudinal Analysis for Disease Progression via Simultaneous Multi-Relational Temporal-Fused Learning24
This paper proposes a joint learning method for predicting clinical scores of patients using multiple longitudinal prediction models for various future time points and shows that the method achieves considerable improvement over competing methods in predicting multiple clinical scores.
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- 3D IFPN: Improved Feature Pyramid Network for Automatic Segmentation of Gastric Tumor23
Experimental results indicate that the proposed 3D IFPN model outperforms state-of-the-art segmentation networks in gastric tumor segmentation and is extended to Liver Tumor Segmentation Challenge to explore the generalization for other segmentation tasks.
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- Robust and secure watermarking scheme for breath sound23
The experimental results demonstrate that the proposed watermarking scheme obtains good robustness against common manipulation attacks and preserves imperceptivity, and the performance comparison results verify that the scheme outperforms existing approaches in terms of robustness and imperceptibility.
- Macroscopic Cerebral Tumor Growth Modeling From Medical Images: A Review22
This paper focuses on the macroscopic growth modeling of brain tumors, mainly glioma, and highlights the current achievements in the state-of-the-art methods.
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- LAC-GAN: Lesion attention conditional GAN for Ultra-widefield image synthesis21
Experimental results on multi-center UWF image datasets demonstrate that the proposed lesion feature attention mechanism can generate retinal images with reasonable details, which helps to enhance the performance of the disease detection model.
- Integrating Similarity Awareness and Adaptive Calibration in Graph Convolution Network to Predict Disease21
A similarity-aware adaptive calibrated GCN (SAC-GCN), which can combine functional and structural information to predict SMC and MCI and is effective to predict disease-induced deterioration and superior over other related algorithm.
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- Domain-Generalized EEG Classification With Category-Oriented Feature Decorrelation and Cross-View Consistency Learning20
A novel domain-generalized EEG classification framework to generalize EEG decoding through category-relevant and -irrelevant Feature Decorrelation and Cross-view invariant feature Learning is proposed and seamlessly integrate three complementary regularizations into a unified DG framework to jointly improve the generalizability and robustness of the model on unseen subjects.
- Cell classification with worse-case boosting for intelligent cervical cancer screening20
This work proposes a new learning algorithm, called worse-case boosting, for classifiers effectively learning from under-representative datasets in cervical cell classification, which aims to learn more from worse- case data for which the classifier has a larger gradient norm compared to other training data.
- Predicting Optical Coherence Tomography-Derived High Myopia Grades From Fundus Photographs Using Deep Learning20
A novel deep learning model to detect and predict myopic maculopathy according to the atrophy, traction, and neovascularisation classification and grading system achieved performance comparable to that of retinal specialists in predicting vision-threatening conditions in high myopia via simple fundus photographs.
- Automatic Prostate Gleason Grading Using Pyramid Semantic Parsing Network in Digital Histopathology20
Inspired by the classical semantic segmentation network, a pyramid semantic parsing network (PSPNet) for automatic prostate Gleason grading is proposed, which achieves the best performance with respect to various evaluation metrics for distinguishing benign from malignant.
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- CNN in CT Image Segmentation: Beyond Loss Function for Exploiting Ground Truth Images20
This work presents a novel and practical method that exploits GT images beyond the loss function by enforcing such two CNNs' feature maps to be consistent, and assesses the proposed method on two data sets, and compares its performance to several competitive methods.
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- Automatic and Efficient Standard Plane Recognition in Fetal Ultrasound Images via Multi-scale Dense Networks20
A general and efficient framework to detect several standard planes from ultrasound scan images or videos automatically and achieves a mean average precision (mAP) of 98.15% with half resources and double speeds in FSPs recognition task is proposed.
- Quality Assessment of Fetal Head Ultrasound Images Based on Faster R-CNN19
This study presents a novel method for the quality assessment of fetal head in ultrasound images based on Faster Region-based Convolutional Neural Networks (Faster R-CNN), which is able to learn and extract features from the training data.
- Prediction of protein-protein interactions by label propagation with protein evolutionary and chemical information derived from heterogeneous network19
A novel computational method based on a similarity network fusion (SNF) model for integrating the physical and chemical properties of proteins, which provides a cost-effective alternative for predicting PPIs.
- Affective parameter shaping in user experience prospect evaluation based on hierarchical Bayesian estimation19
The cumulative prospect theory quantitatively fulfills user experience evaluation by developing a hierarchical Bayesian model via Markov chain Monte Carlo technique for parameter estimation under three affective states and demonstrating the proposed method via a aircraft cabin interior design.
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- BDHT: Generative AI Enables Causality Analysis for Mild Cognitive Impairment17
This work aims to provide a unified framework for the estimation of brain effective connectivity using generative artificial intelligence and enables practitioners to develop deep learning model-based medical tools to assist clinicians with disease diagnosis and pathological analysis in an efficient way.
- TA-GAN: transformer-driven addiction-perception generative adversarial network17
An adaptive discriminator enhancement mechanism is proposed that allows the discriminator to acquire addiction-related brain connections with limited data volume efficiently and achieves promising results in both qualitative and quantitative measurements.
- Convolutional 3D to 2D Patch Conversion for Pixel-Wise Glioma Segmentation in MRI Scans17
Experimental results on the segmentation of brain tumors in multimodal MRI scans (BraTS’19) demonstrate that the proposed method can efficiently segment the tumor regions.
- Deeply supervised full convolution network for HEp-2 specimen image segmentation17
A novel deeply supervised full convolutional network (DSFCN) is proposed for robust segmentation of different HEp-2 cell images dataset and the result demonstrates that the performance of the model outperforms other state-of-the-art methods.
- Longitudinal score prediction for Alzheimer’s disease based on ensemble correntropy and spatial–temporal constraint17
An effective framework using a novel feature selection model via sparse learning to reduce the adverse effect of noise and outliers, and promote consistent and robust selection of features by exploring data characteristics is presented.
- A Dense Phase Descriptor for Human Ear Recognition17
The proposed descriptor, namely, dense local phase quantization (DLPQ) is based on the phase responses, which is generated using the well-known LPQ descriptor, and stands out among recent ear descriptors from the literature.
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- Channel–Spatial Segmentation Network for Classifying Leaf Diseases16
The Channel–Spatial segmentation network can be used effectively to diagnose different disease degrees based on a combination of image processing and statistical calculation.
- An optimized generic cerebral tumor growth modeling framework by coupling biomechanical and diffusive models with treatment effects16
An optimized generic modeling framework that couples tumor diffusivity and infiltration with the induced mass effect and optimize the tumor growth parameters to be patient-specific using bio-inspired particle swarm optimization (PSO) algorithm is proposed.
- Automated Segmentation of Overlapping Cytoplasm in Cervical Smear Images via Contour Fragments16
A novel method for automated segmentation of overlapping cytoplasm in cervical smear images based on contour fragments is presented, which can take more geometric information into account and hence generate more accurate prediction of the overlapping boundaries.
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- Federated learning using model projection for multi-center disease diagnosis with non-IID data15
Extensive experimental results show that the FedMoP outperforms state-of-the-art FL methods in terms of accuracy, convergence rate and communication cost, and achieves comparable or even higher accuracy than centralized learning.
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- Constructing Condition Monitoring Model of Wind Turbine Blades15
A method that could determine structural damage or weaknesses in the blades at an early stage via a model to monitor the sound of the wind turbine blades, so as to reduce the quantity of labor required and frequency of regular maintenance, and to repair the damage rapidly in the future.
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- Adversarial learning-based multi-level dense-transmission knowledge distillation for AP-ROP detection14
A novel adversarial learning-based multi-level dense knowledge distillation method for detecting AP-ROP, where the pre-trained teacher network is utilized to train multiple intermediate-size networks and one student network by dense transmission mode.
- A Point Cloud Generative Model via Tree-Structured Graph Convolutions for 3D Brain Shape Reconstruction14
A general generative adversarial network (GAN) architecture based on graph convolutional networks is proposed to reconstruct the 3D point clouds of brains by using one single 2D image, thus relieving the limitation of acquiring 3D shape data during surgery.
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- SPRNet: Automatic Fetal Standard Plane Recognition Network for Ultrasound Images14
This work adopts DenseNet as the basic network of SPRNet and implements data-based partial transfer learning on it by weight-sharing strategy and achieves automatic fetal standard plane recognition by utilizing the feature extracted from SPRNet.
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- Early diagnosis and clinical score prediction of Parkinson's disease based on longitudinal neuroimaging data13
A network combining feature selection method with feature learning to obtain the most discriminative feature representation for longitudinal early diagnosis and clinical score prediction compared to state-of-the-art methods is proposed.
- Diagnosis of obsessive-compulsive disorder via spatial similarity-aware learning and fused deep polynomial network13
A novel framework is proposed to detect OCD and unaffected first-degree relatives (UFDRs), which combines deep learning and traditional machine learning methods, and can solve the problem of curse of dimensionality.
- Joint segmentation and classification task via adversarial network: Application to HEp-2 cell images13
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- Assessment of liver fibrosis in chronic hepatitis B via multimodal data13
The proposed method outperforms any single method and their linear combination and also achieves a state-of-the-art performance, which is very promising for staging liver fibrosis from multimodal information.
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- Federated Pseudo Modality Generation for Incomplete Multi-Modal MRI Reconstruction12
This paper proposes a novel communication-efficient federated learning framework (namely Fed-PMG) to address the missing modality challenge in federated multi-modal MRI reconstruction and demonstrates that the proposed method can outperform state-of-the-art methods and reach a performance similar to the ideal scenario.
- GIONet: Global information optimized network for multi-center COVID-19 diagnosis via COVID-GAN and domain adversarial strategy12
A COVID-19 classification model using global information optimized network (GIONet) and cross-centers domain adversarial learning strategy and a 3D convolutional neural network with graph enhanced aggregation unit and multi-scale self-attention fusion unit is designed to improve the global feature extraction capability.
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- Automated Stage Analysis of Retinopathy of Prematurity Using Joint Segmentation and Multi-instance Learning12
This work combines the segmentation and staging, using both fully convolutional network (FCN) and multi-instance learning (MIL) to achieve integrated task of ROP staging and lesions localization.
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- Multi-modal and multi-layout discriminative learning for placental maturity staging12
This work proposes a method to automatically determine the placental maturity by harnessing multi-view and multi-layout discriminative learning fusion, and applies a multi- layout feature encoding method to improve the staging performance using discrim inative learning technique.
- Joint Learning of Multiple Longitudinal Prediction Models by Exploring Internal Relations12
The performance of the joint prediction models with the data from the Alzheimer's Disease Neuroimaging Initiative ADNI database is evaluated, showing much better performance than the state-of-the-art methods in predicting multiple clinical scores at multiple future time points.
- Automatic staging of placental maturity based on dense descriptor12
Dense sampled DAISY descriptor is investigated for the demonstrated scale and translation invariant properties and experimental results show that the dense features outperform the sparse features.
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- A Noise-Robust Online convolutional coding model and its applications to poisson denoising and image fusion11
Experimental results show that the proposed noise-robust online convolutional coding model for image representation can output more meaningful feature representations compared to the related models while the training data was corrupted by Poisson noise.
- Learning 3D Features with 2D CNNs via Surface Projection for CT Volume Segmentation11
This paper proposes a simple and effective technique that allows a 2D CNN to learn 3D features for segmenting CT volumes by learning the projection distance between a set of prescribed spherical surfaces and the object’s surface, which can be readily performed by a2D CNN.
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- A multipurpose audio watermarking algorithm with synchronization and encryption11
Experimental results show that the proposed multipurpose audio watermarking scheme can not only obtain satisfactory detection and tampered location, but also achieve imperceptibility and robustness to common signal processing attacks, such as cropping, shifting, and time scale modification (TSM).
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- Multi-scale Graph Convolutional Network for Mild Cognitive Impairment Detection9
This paper proposes a novel method based on multi-scale graph convolutional network (MS-GCN) via inception module, which combines image and non-image information for MCI detection, and demonstrates that the proposed method can achieve remarkableMCI detection performance.
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- Quality evaluation of induced pluripotent stem cell colonies by fusing multi-source features8
Experimental results show that the proposed automatic quality evaluation model could obtain 95.55% classification accuracy, which could provide a method to efficiently and quickly judge the biological quality of a single iPSC colony or populations and facilitate the large-scale iPSCs manufacturing.
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- Gene-related Parkinson's disease diagnosis via feature-based multi-branch octave convolution network7
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- Evaluation of fragility fracture risk using deep learning based on ultrasound radio frequency signal6
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- Shape Mask Generator: Learning to Refine Shape Priors for Segmenting Overlapping Cervical Cytoplasms6
A conceptually simple and effective technique, called shape mask generator, for segmenting overlapping cytoplasms, which model shape priors from shape templates and feed them to the shape mask generators that generates a shape mask for the cytoplasm as the segmentation result.
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- Feature fusion network for pulmonary nodule segmentation and EGFR classification using dual encoders4
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- Longitudinal Feature Selection and Feature Learning for Parkinson's Disease Diagnosis and Prediction4
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- Aggressive Posterior Retinopathy of Prematurity Automated Diagnosis via a Deep Convolutional Network4
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- Clip-Driven Universal Model for Multi-Material Decomposition in Dual-Energy CT3
A CLIP-Driven Universal Model for adaptive Multi-Material Decomposition (MMD) that utilizes the semantic capabilities of text embeddings from Contrastive Language-Image Pre-training (CLIP), allowing a single network to manage structured feature embedding for multiple materials.
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- Federated Multi-source Domain Adaptation via Vision Transformer for Multi-site Alzheimer’s Diagnosis2
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- Dual-Branch Multimodal Fusion Network for Skin Lesions Diagnosis using Clinical and Ultrasound Image2
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- Gated CNN-Transformer Network for Automatic Cardiovascular Diagnosis using 12-lead Electrocardiogram1
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-16. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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