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Works468 from public data
- 836
- Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease634
This framework uses a zero-masking strategy for data fusion to extract complementary information from multiple data modalities to aid the diagnosis of AD and has the potential to require less labeled data.
- Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization592
A new clustering model, called DEeP Embedded Regularized ClusTering (DEPICT), which efficiently maps data into a discriminative embedding subspace and precisely predicts cluster assignments is proposed, which indicates the superiority and faster running time of DEPICT in real-world clustering tasks, where no labeled data is available for hyper-parameter tuning.
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- Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis402
This paper provides an effective implementation of the proposed aggregation strategy including a novel curve grouping operator followed by a curve aggregation operator for aggregating hypothetical curves in point clouds.
- Content-Based Medical Image Retrieval: A Survey of Applications to Multidimensional and Multimodality Data234
This paper presents a review of state-of-the-art medical CBIR approaches in five main categories: two-dimensional image retrieval, retrieval of images with three or more dimensions, the use of nonimage data to enhance the retrieval, multimodality image retrieved, and retrieval from diverse datasets.
- A speculative approach to spatial‐temporal efficiency with multi‐objective optimization in a heterogeneous cloud environment209
An adaptive method aiming at spatial-temporal efficiency in a heterogeneous cloud environment based on an optimized Kernel-based Extreme Learning Machine algorithm is presented for faster forecast of job execution duration and space occupation and achieves 26.6% improvement over the original scheme.
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- NFN＋: A novel network followed network for retinal vessel segmentation190
The proposed NFN＋ model, to the best knowledge, achieved the state-of-the-art retinal vessel segmentation accuracy on color fundus images (AUC: 98.30%, 98.75% and 98.94%, respectively).
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- Multiscale Network Followed Network Model for Retinal Vessel Segmentation169
The proposed multiscale network followed network (MS-NFN) model achieved the state-of-the-art retinal vessel segmentation accuracy on both datasets.
- 3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes161
A 3D Anisotropic Hybrid Network (AH-Net) that transfers convolutional features learned from 2D images to 3D anisotropic volumes and inherits the desired strong generalization capability for within- slice information while naturally exploiting between-slice information for more effective modelling is proposed.
- Vessel-Net: Retinal Vessel Segmentation Under Multi-path Supervision155
A novel and lightweight deep learning model called Vessel-Net for retinal vessel segmentation, which designs an efficient inception-residual convolutional block to combine the advantages of the Inception model and residual module for improved feature representation and introduces four supervision paths to preserve the rich and multi-scale deep features during model optimization.
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- DeepGene: an advanced cancer type classifier based on deep learning and somatic point mutations139
DeepGene, an advanced cancer type classifier, is proposed, which addresses the obstacles in existing SMCC studies and outperforms three widely adopted existing classifiers, mainly attributed to its deep learning module that is able to extract the high level features between combinatorial somatic point mutations and cancer types.
- 137
- CelebV-Text: A Large-Scale Facial Text-Video Dataset129
The effectiveness and potential of CelebV- Text are shown through extensive self-evaluation, and a benchmark is constructed with representative methods to standardize the evaluation of the facial text-to-video generation task.
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- BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder Architecture108
A novel Bi-directional O-shape network (BiO-Net) that reuses the building blocks in a recurrent manner without introducing any extra parameters that significantly outperforms the vanilla U-Net as well as other state-of-the-art methods.
- Robust, accurate and efficient face recognition from a single training image: A uniform pursuit approach107
Experimental results show that the robustness, accuracy and efficiency of the proposed UP method compare favorably to the state-of-the-art one sample based methods.
- A Deep Learning-Based Algorithm Identifies Glaucomatous Discs Using Monoscopic Fundus Photographs106
This study demonstrated that a deep learning-based algorithm can identify glaucomatous discs at high accuracy level using monoscopic fundus images, highlighting the algorithm's potential application in large population-based disease screening or telemedicine programs.
- Whole brain white matter connectivity analysis using machine learning: An application to autism105
An automated white matter connectivity analysis method for machine learning classification and characterization of white matter abnormality via identification of discriminative fiber tracts and the results indicate the potential of a machine learning pipeline based on white matter fiber clustering.
- 89
- Breaking the Modality Barrier: Universal Embedding Learning with Multimodal LLMs88
UniME (Universal Multimodal Embedding), a novel two-stage framework that leverages MLLMs to learn discriminative representations for diverse downstream tasks, and introduces hard negative enhanced instruction tuning to further advance discriminative representation learning.
- A review of image watermarking for identity protection and verification88
This review prominently covers the image watermarking works that have positively influenced its relevant research area and proposes a new systematisation of classifying existing watermarking methods.
- Exploiting Edge-Oriented Reasoning for 3D Point-based Scene Graph Analysis88
A 3D point-based scene graph generation (SGGpoint) framework to effectively bridge perception and reasoning to achieve scene under-standing via three sequential stages, namely scene graph construction, reasoning, and inference is proposed.
- Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-Weighting87
Experimental results indicate that the proposed Cycle Consistency Panoptic Domain Adaptive Mask R-CNN architecture for unsupervised nuclei segmentation in histopathology images outperforms state-of-the-art UDA methods significantly, and demonstrates a similar performance as fully supervised methods.
- Atlas registration and ensemble deep convolutional neural network-based prostate segmentation using magnetic resonance imaging84
This work presents a registration-based coarse segmentation to the pre-processed prostate MR images to get the potential boundary region, and trains deep neural networks as pixel-based classifier to predict whether the pixel in the possible boundary region is prostate pixel or not, and introduces ensemble learning for fine segmentation.
- Rivulet: 3D Neuron Morphology Tracing with Iterative Back-Tracking81
The proposed Rivulet algorithm, based on the multi-stencils fast-marching and iterative back-tracking, is capable of tracing discontinuous areas without being interrupted by densely distributed noises.
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- Optimizing the cervix cytological examination based on deep learning and dynamic shape modeling80
A robust variational segmentation framework based on superpixelwise convolutional neutral network and a learned shape prior enabling an accurate analysis of overlapping cervical mass is presented, demonstrating that the methodology can successfully segment nuclei and cytoplasm from highly overlapping mass.
- SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection79
It is shown that SQUID can taxonomize the ingrained anatomical structures into recurrent patterns; and in the inference, it can identify anomalies in the image and surpasses 13 state-of-the-art methods in unsupervised anomaly detection.
- Transferable Multi-model Ensemble for Benign-Malignant Lung Nodule Classification on Chest CT79
The transferable multi-model ensemble (TMME) algorithm is proposed to separate malignant from benign lung nodules using limited chest CT data and achieves a lung nodule classification accuracy of 93.40%, which is markedly higher than the accuracy of seven state-of-the-art approaches.
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- Decompose to Adapt: Cross-Domain Object Detection Via Feature Disentanglement70
A Domain Disentanglement Faster-RCNN (DDF) is designed to eliminate the source-specific information in the features for detection task learning and is demonstrated to be effective with wide applicability.
- H$$^2$$NF-Net for Brain Tumor Segmentation Using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Task70
The H2NF-Net uses the single and cascaded HNF-Nets to segment different brain tumor sub-regions and combines the predictions together as the final segmentation and won the second place in the BraTS 2020 challenge segmentation task.
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- Automatic segmentation of overlapping cervical smear cells based on local distinctive features and guided shape deformation69
The results show that the proposed automated segmentation method for the nucleus and cytoplasm in a cluster of cervical cells based on distinctive local features and guided sparse shape deformation outperformed existing approaches in segmenting overlapping cells and obtaining accurate nuclear boundaries.
- Robust Capped Norm Nonnegative Matrix Factorization: Capped Norm NMF69
A novel robust capped norm orthogonal Nonnegative Matrix Factorization model is presented, which utilizes the capped norm for the objective to handle these extreme outliers and a new efficient optimization algorithm is derived to solve the proposed non-convex non-smooth objective.
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- Superficial white matter analysis: An efficient point-cloud-based deep learning framework with supervised contrastive learning for consistent tractography parcellation across populations and dMRI acquisitions58
A novel two-stage deep-learning-based framework, Superficial White Matter Analysis (SupWMA), is proposed that performs an efficient and consistent parcellation of 198 SWM clusters from whole-brain tractography and obtains highly consistent and accurate SWM parcellation results on all datasets.
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- Panoptic Segmentation with an End-to-End Cell R-CNN for Pathology Image Analysis54
The results demonstrate that the proposed method not only outperforms state-of-the-art approaches to the 2017 MICCAI Digital Pathology Challenge dataset, but also proposes an effective and end-to-end solution for the panoptic segmentation challenge.
- HD-Net: Hybrid Discriminative Network for Prostate Segmentation in MR Images53
The Hybrid Discriminative Network (HD-Net), which consists of a 3D segmentation decoder using channel attention block to generate semantically consistent volumetric features and an auxiliary 2D boundary decoder guiding the segmentation network to focus on the semantically discriminative intra-slice features, is proposed.
- Complex Organ Mask Guided Radiology Report Generation52
The Complex Organ Mask Guided (termed as COMG) report generation model is proposed, which incorporates masks from multiple organs to pro-vide more detailed information and guide the model’s attention to these crucial body regions.
- BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets52
This resource describes a collection of neurons from a variety of light microscopy-based datasets, which can serve as a gold standard for testing automated tracing algorithms, as shown by comparison of the performance of 35 algorithms.
- Nuclei Segmentation via a Deep Panoptic Model with Semantic Feature Fusion52
This work proposes a panoptic segmentation model which incorporates an auxiliary semantic segmentation branch with the instance branch to integrate global and local features and designs a feature map fusion mechanism in the instance Branch and a new mask generator to prevent information loss.
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- UniME-V2: MLLM-as-a-Judge for Universal Multimodal Embedding Learning49
The MLLM-as-a-Judge mechanism is introduced, which utilizes MLLMs to assess the semantic alignment of query-candidate pairs and generate soft semantic matching scores, which serve as a foundation for hard negative mining, mitigating the impact of false negatives and enabling the identification of diverse, high-quality hard negatives.
- Multiple Sclerosis Lesion Analysis in Brain Magnetic Resonance Images: Techniques and Clinical Applications49
A comprehensive review of state-of-the-art automatic statistical and deep-learning MS segmentation methods and technical strategies, such as domain adaptation, to enhance MS lesion segmentation in real-world clinical settings are reviewed.
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- Supervised Intra-embedding of Fisher Vectors for Histopathology Image Classification44
The results demonstrate that the supervised intra-embedding method helps to enhance the ConvNet-based Fisher vectors effectively, and the classification results largely outperform the state-of-the-art approaches on these datasets.
- Multifold Bayesian Kernelization in Alzheimer’s Diagnosis44
A novel diagnosis algorithm, the Multifold Bayesian Kernelization (MBK), which models the diagnosis process as a synthesis analysis of multi-modal biomarkers, and adopts a novel diagnosis scheme that could infer the subject’s diagnosis by synthesizing the output diagnosis probabilities of individual biomarkers.
- Real-Time Volume Rendering Visualization of Dual-Modality PET/CT Images With Interactive Fuzzy Thresholding Segmentation41
A fuzzy thresholding segmentation technique based on fuzzy cluster analysis is presented and validated, which allows interactive and real-time optimization of the segmentation results and is incorporated into a real- time multi-volume rendering of dual-modality PET/CT images.
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- Dictionary pruning with visual word significance for medical image retrieval40
This work proposes a retrieval method based on a bag-of-visual-words (BoVW) to identify discriminative characteristics between different medical images with Pruned Dictionary based on Latent Semantic Topic description.
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- LFACon: Introducing Anglewise Attention to No-Reference Quality Assessment in Light Field Space38
A novel concept of “anglewise attention” is proposed by introducing a multihead self-attention mechanism to the angular domain of an LFl to better reflects the LFI quality and the experimental results show that the proposed LFACon metric significantly outperforms the state-of-the-art LFIQA metrics.
- Deep Learning Methodology for Differentiating Glioma Recurrence From Radiation Necrosis Using Multimodal Magnetic Resonance Imaging: Algorithm Development and Validation38
The proposed ERN-Net model, a simple and effective DNN model, achieved excellent performance on routine MRI scans and showed a high clinical applicability.
- Early identification of mild cognitive impairment using incomplete random forest-robust support vector machine and FDG-PET imaging38
This work proposes a semi-supervised learning method based on robust optimization for the identification of MCI fromfluorodeoxyglucose PET scans that outperformed the other techniques in the ability to separate MCIFrom normal controls and compared to other state-of-the-art methods in different learning schemas.
- Region-based progressive localization of cell nuclei in microscopic images with data adaptive modeling38
A region-based progressive localization method is proposed for cell nuclei localization in fluorescence microscopy images that is demonstrated highly effective on three different public datasets, with on average 3.5% and 7% improvement of region- and contour-based segmentation performance over the state-of-the-art.
- Suprathreshold fiber cluster statistics: Leveraging white matter geometry to enhance tractography statistical analysis37
The results indicate that the STFC method gives greater sensitivity in finding group differences in white matter tract parcels compared to several traditional multiple comparison correction methods.
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- Deep fiber clustering: Anatomically informed fiber clustering with self-supervised deep learning for fast and effective tractography parcellation36
This work proposes a novel deep learning framework for white matter fiber clustering, Deep Fiber Clustering (DFC), which solves the unsupervised clustering problem as a self-supervised learning task with a domain-specific pretext task to predict pairwise fiber distances.
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- Chitosan modified Fe3O4/KGN self-assembled nanoprobes for osteochondral MR diagnose and regeneration35
Using the Fe3O4-CS/KGN nanoprobe, it is easy to distinguish the defect position, and it could act as a tool for dynamic observation as well as a stem cell-based therapy for directionally chondral differentiation.
- Integrating Heterogeneous Brain Networks for Predicting Brain Disease Conditions35
A graph convolutional network (GCN) framework is developed to integrate heterogeneous brain networks and achieves state-of-the-art prediction results, and validates the advantages of the utilization of multi-modal brain networks.
- Lung Nodule Classification by Jointly Using Visual Descriptors and Deep Features35
The texture and shape descriptors, which characterize the heterogeneity of nodules, and the features learned by a deep convolutional neural network are used, and thus a combined-feature based classification (CFBC) algorithm is proposed to differentiate lung nodules.
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- Cancer type prediction based on copy number aberration and chromatin 3D structure with convolutional neural networks34
The advantages and potential of the proposed DeepCNA model for processing of somatic point mutation based gene data are demonstrated, and it is proposed that its usage may be extended to other complex genotype-phenotype association studies.
- Locality-constrained Subcluster Representation Ensemble for lung image classification34
A new Locality-constrained Subcluster Representation Ensemble model, to classify high-resolution computed tomography images of interstitial lung diseases (ILDs), which shows good performance improvement over existing popular classifiers.
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- The effect of device resistance and inhalation flow rate on the lung deposition of orally inhaled mannitol dry powder33
In conclusion, the resistance of Osmohaler™ did not significantly affect the total and regional lung deposition at 50-70L/min PIFR, and the totaland regional lung depositions are dependent on the particle size of the aerosol and inhalation flow rate, the latter itself affecting the particles size distribution.
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- DeepAntigen: a novel method for neoantigen prioritization via 3D genome and deep sparse learning32
It is discovered that DNA loci of the Immuno-positive and immuno-negative MHC-I neoantigens have distinct spatial distribution patterns across the genome, and a group feature selection based deep sparse neural network model (DNN-GFS) that is optimized for neoantigen prioritization is developed.
- BiX-NAS: Searching Efficient Bi-directional Architecture for Medical Image Segmentation31
This work study a multi-scale upgrade of a bi-directional skip connected network and then automatically discover an efficient architecture by a novel two-phase Neural Architecture Search (NAS) algorithm, namely BiX-NAS, which achieves the state-of-the-art performance with significantly lower computational cost.
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- Combination Therapy Using Kartogenin-Based Chondrogenesis and Complex Polymer Scaffold for Cartilage Defect Regeneration30
A novel multidimensional combination therapy of cartilage defect repair that facilitated cartilage regeneration and has a great clinical translational potential for articular cartilage repair in the near future is developed.
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- Multimodal neuroimaging computing: the workflows, methods, and platforms30
This workflows and methods for multimodal neuroimaging computing are reviewed, and also how to conduct research using the established neuroim imaging computing packages and platforms are demonstrated.
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- Deep Fiber Clustering: Anatomically Informed Unsupervised Deep Learning for Fast and Effective White Matter Parcellation29
A convolutional neural network is used to learn embeddings of input fibers, using pairwise fiber distances as pseudo annotations, which enables WMFC that is insensitive to fiber point ordering and enables outlier removal in a natural way.
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- A convolutional neural network with pixel-wise sparse graph reasoning for COVID-19 lesion segmentation in CT images28
A segmentation network with a novel pixel-wise sparse graph reasoning (PSGR) module for the segmentation of COVID-19 infected regions in CT images and the segmentation model equipped with this PSGR module can accurately segment COVID-19 infected regions in CT images and outperform all other competing models.
- Celltrack R-CNN: A Novel End-To-End Deep Neural Network For Cell Segmentation And Tracking In Microscopy Images28
This study proposes a novel approach to combine cell segmentation and cell tracking into a unified end-to-end deep learning based framework, where cell detection and segmentation are performed with a current instance segmentation pipeline and celltracking is implemented by integrating Siamese Network with the pipeline.
- Multi-Channel neurodegenerative pattern analysis and its application in Alzheimer's disease characterization28
A Multi-Channel pattern analysis approach is proposed to identify the most discriminative local brain metabolism features for neurodegenerative disorder characterization and provided important insights into the underlying pathology of Alzheimer's disease and Mild Cognitive Impairment.
- Triple-Crossing 2.5D Convolutional Neural Network for Detecting Neuronal Arbours in 3D Microscopic Images27
A novel Triple-Crossing (TC) 2.5D convolutional neural network is presented to detect the neuronal arbours in large 3D microscopic volumes with a reasonable computational cost and could outperform the original 2.
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- Bioimage classification with subcategory discriminant transform of high dimensional visual descriptors27
A general bioimage classification method, which comprises a highly descriptive visual feature representation and a learning-based discriminative feature transformation algorithm, which demonstrates improved performance over the state-of-the-art for six different classification tasks.
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- Texture image classification with discriminative neural networks26
A discriminative neural network-based feature transformation (NFT) method is designed, with which the CNN-based features are transformed to lower dimensionality descriptors based on an ensemble of neural networks optimized for the classification objective.
- Pairwise Latent Semantic Association for Similarity Computation in Medical Imaging26
A new feature extraction method for similarity computation in medical imaging that design a CCA-PairLDA feature representation method to capture the similarity between images with high-level semantics.
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- Hierarchical spatial matching for medical image retrieval26
This work aims to effectively extract and represent the spatial context of pathological tissues, and design a novel hierarchical spatial matching (HSM) method based on the spatial pyramid matching.
- Emulating biological strategies for uncontrolled face recognition26
It is shown that a simple system designed by emulating biological strategies of human visual system can largely surpass the state-of-the-art performance on uncontrolled face recognition.
- Exploiting Structural Consistency of Chest Anatomy for Unsupervised Anomaly Detection in Radiography Images25
This work proposes a Simple Space-Aware Memory Matrix for In-painting and Detecting anomalies from radiography images (abbreviated as SimSID), formulate anomaly detection as an image reconstruction task, consisting of a space-aware memory matrix and an in-painting block in the feature space.
- Prediction of the Fatigue Strength of Steel Based on Interpretable Machine Learning25
A predictive model for FS based on machine learning (ML), which showed a good performance, was established and Shapley additive explanations (SHAP) and symbolic regression (SR) are introduced to improve the interpretability of the prediction model.
- Supwma: Consistent and Efficient Tractography Parcellation of Superficial White Matter with Deep Learning25
A deep-learning-based framework, Superficial White Matter Analysis (SupWMA), that performs an efficient and consistent parcellation of 198 SWM clusters from whole-brain tractography, and the computational speed of SupWMA is much faster than other methods.
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- Discriminative Pathological Context Detection in Thoracic Images Based on Multi-level Inference25
A discriminative, multi-level learning and inference method to automatically detect the pathological contexts in the thoracic PET-CT images, i.e. the primary tumor and its spatial relationships within the lung and mediastinum, and disease in regional lymph nodes is proposed.
- Optimal Design of the Austenitic Stainless-Steel Composition Based on Machine Learning and Genetic Algorithm24
The NSGA-III algorithm was used to simultaneously maximize the mechanical property prediction models within the search space, thereby obtaining the corresponding non-dominated solution set of chemical composition and achieving the optimization of austenitic stainless-steel compositions.
- Learning High-Resolution and Efficient Non-local Features for Brain Glioma Segmentation in MR Images24
A 3D High-resolution and Non-local Feature Network for brain glioma segmentation using multi-parametric MR imaging based on the parallel multi-scale fusion (PMF) module and the expectation-maximization attention (EMA) module is introduced, aiming to capture the long-range dependent contextual information and reduce the feature redundancy in a lightweight fashion.
- Localized Sparse Code Gradient in Alzheimer's disease staging24
The proposed new AD staging method embedded the high dimensional multi-view features derived from neuroimaging data into a low dimensional feature space and could form a more distinctive representation than the naive concatenated features.
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- Generalized linear least squares algorithms for modeling glucose metabolism in the human brain with corrections for vascular effects24
The GLLS algorithm is extended to a five parameter model for the study of human brain metabolism, which accounts for the effect of cerebral blood volume (CBV), using OISS sampled data, with as few as five temporal samples.
- LaPA: Latent Prompt Assist Model for Medical Visual Question Answering23
A multi-modal fusion block with latent prompt fusion module that utilizes the latent prompt to extract clinical-relevant information from uni-modal and multi-modal features and introduces a prior knowledge fusion module to integrate the relationship between diseases and organs with the clinical-relevant information.
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- HNF-Netv2 for Brain Tumor Segmentation Using Multi-modal MR Imaging23
This paper extends the HNF-Net to H NF-Netv2 by adding inter-scale and intra-scale semantic discrimination enhancing blocks to further exploit global semantic discrimination for the obtained high-resolution features.
- An adaptive approach to better load balancing in a consumer-centric cloud environment23
Experimental results depict that the proposed scheme can allocate tasks evenly and improve work efficiency in a heterogeneous cluster, and an adaptive solution to optimize the performance of space-time is obtained.
- Estimation Accuracy on Execution Time of Run-Time Tasks in a Heterogeneous Distributed Environment23
A two-phase regression (TPR) method is proposed to predict the finishing time of each task accurately, and the prediction accuracy of concurrent tasks’ execution time can be improved, in particular for some regular jobs.
- A web-based collaborative system for medical image analysis and diagnosis23
The development of a web-based collaborative system for medical image analysis and diagnosis that is affordable, usable, reliable and efficient for medical area is shown.
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- PaRot: Patch-Wise Rotation-Invariant Network via Feature Disentanglement and Pose Restoration20
A novel Patch-wise Rotation-invariant network (PaRot), which achieves rotation invariance via feature disentanglement and produces consistent predictions for samples with arbitrary rotations and achieves competitive results in rotated 3D object classification and part segmentation tasks.
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- SVM Based Classification and Prediction System for Gastric Cancer Using Dominant Features of Saliva20
This study proposed a classification and prediction system for the diagnosis of gastric cancer based on saliva samples that achieved an overall accuracy of 97.18%, specificity, sensitivity, and sensitivity of 96.88% and owns the prospect of clinical translation.
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- Multiple sclerosis lesion segmentation: revisiting weighting mechanisms for federated learning19
This work proposes the first FL MS lesion segmentation framework via two effective re-weighting mechanisms, where a learnable weight is assigned to each local node during the aggregation process, based on its segmentation performance.
- Super-resolution spectral estimation in short-time non-contact vital sign measurement19
Both simulated and experimental results show that the proposed algorithm has superior performance to DFT-based methods and the recently applied multiple signal classification algorithm, and the required processing window length has been shortened to 5.12 s.
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- ICE-GAN: Identity-aware and Capsule-Enhanced GAN for Micro-Expression Recognition and Synthesis.18
This work proposes a novel Identity-aware and Capsule-Enhanced Generative Adversarial Network (ICE-GAN), which is adversarially completed with the micro-expression synthesis (MES) task, where synthetic faces with controllable micro-expressions can be produced by the generator with distinguishable identity information to improve the MER performance.
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- The Shape of the Brain's Connections Is Predictive of Cognitive Performance: An Explainable Machine Learning Study17
This work implements two machine learning models (1D‐CNN and Least Absolute Shrinkage and Selection Operator [LASSO]) to predict individual cognitive performance scores and demonstrates the strong potential of shape descriptors to enhance the study of the brain's white matter and its relationship to cognitive function.
- Cross-View Consistency Regularisation for Knowledge Distillation17
This work introduces within-view and cross-view regularisations to standard logit-based distillation frameworks to combat the above cruxes and performs confidence-based soft label mining to improve the quality of distilling signals from the teacher, which further mitigates the confirmation bias problem.
- Multi-Phase Feature Representation Learning for Neurodegenerative Disease Diagnosis17
This study proposed a novel feature representation learning framework, Multi-Phase Feature Representation (MPFR), with low-dimensional biomarkers embedded that outperformed the original neural network in both binary and ternary Alzheimer’s disease classification tasks.
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- Shape-Oriented Convolution Neural Network for Point Cloud Analysis16
A shape-oriented message passing scheme dubbed ShapeConv is proposed to focus on the representation learning of the underlying shape formed by each local neighboring point, designed to incorporate the contextual effects from the inter-shape relationship through capturing the long-ranged dependencies between local underlying shapes.
- Automated 3D Soma Segmentation with Morphological Surface Evolution for Neuron Reconstruction16
This paper presents a novel method to segment the soma structures with complex geometry and shows that the proposed method can outperform the existing soma segmentation methods regarding the accuracy and can be used for enhancing the results of existing neuron tracing methods.
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- Discriminative Data Transform for Image Feature Extraction and Classification16
This paper presents a novel feature design that is applicable to different application domains, and is evaluated on both lung tissue classification in high-resolution computed tomography images and apoptosis detection in time-lapse phase contrast microscopy image sequences.
- A Bag of Semantic Words Model for Medical Content-based Retrieval16
The preliminary results show that the proposed bag of semantic words model could extract the semantic information from medical images and outperformed the state-of-the-art medical content-based retrieval methods.
- A New Sparse Simplex Model for Brain Anatomical and Genetic Network Analysis16
This paper proposes a novel sparse simplex model to accurately construct the brain anatomical and genetic networks, which are important to reveal the brain spatial expression patterns, and validates the new model using the 4D ABA data.
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- PointNeuron: 3D Neuron Reconstruction via Geometry and Topology Learning of Point Clouds15
A novel framework for 3D neuron reconstruction that adopts one graph convolutional network to predict the neural skeleton points and another one to produce the connectivity of these points to achieve competitive neuron reconstruction performance.
- Temporal Correlation Structure Learning for MCI Conversion Prediction15
A novel deep learning framework is proposed that uncovers the temporal correlation structure between adjacent time points in the disease progression and builds a generative framework to learn the inherent data distribution so as to produce more reliable data to strengthen the training process.
- 3D Large Kernel Anisotropic Network for Brain Tumor Segmentation15
A 3D large kernels in the decoders ensure the valid receptive field is large enough and the anisotropic convolutional blocks in the encoders simulate the traditional isotropic ones with fewer parameters.
- Machine Learning in Multimodal Medical Imaging15
This paper presents a meta-modelling system that automates the very labor-intensive and therefore time-heavy and expensive and expensive process of computer programming called “crowd-sourcing”.
- Cross-View Neuroimage Pattern Analysis in Alzheimer's Disease Staging15
Nine types of neuroimaging biomarkers derived from FDG-PET and T1-MRI showed that these biomarkers could depict the pathology of AD from different perspectives, and output distinct patterns that are significantly associated with the disease progression, and these features could be separated into clusters.
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- TractGraphFormer: Anatomically informed hybrid graph CNN-transformer network for interpretable sex and age prediction from diffusion MRI tractography14
This work introduces TractGraphFormer, a hybrid Graph CNN-Transformer deep learning framework tailored for diffusion MRI tractography that suggests that widespread connections in the WM are predictive of the sex and age of an individual.
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- Determination of phase-space reconstruction parameters of chaotic time series14
A new method called C-C-1 method is suggested, which can improve some drawbacks of the original C- C method, which is based on the theory of period N and the estimates of the optimaldelay time and the optimal delay time window are more appropriate.
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- [Study on the occurrence of cesarean section (CS) and factors related to CS in China].14
The high rate of CS was more likely to associate not only with abnormal physiological/medical factors (eg. cephalopelvic disproportion, intrauterine asphysia, abnormality of force of labor, and prolonged labour, etc.), but also with some demographic factors as education, occupation, income and age, etc.
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- Learning to Generalize over Subpartitions for Heterogeneity-Aware Domain Adaptive Nuclei Segmentation12
A two-stage disentanglement framework is proposed to acquire domain-invariant feature representations at both image and instance levels and two regularization strategies are specifically devised herein to leverage the rich subpartition-specific characteristics in histopathology images and facilitate subdomain decomposition.
- Taxonomy Adaptive Cross-Domain Adaptation in Medical Imaging via Optimization Trajectory Distillation12
This work proposes optimization trajectory distillation, a unified approach to address the two technical challenges from a new perspective that exploits the low-rank nature of gradient space and devises a dual-stream distillation algorithm to regularize the learning dynamics of insufficiently annotated domain and classes with the external guidance obtained from reliable sources.
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- Biological impact of nanodiamond particles – label free, high-resolution methods for nanotoxicity assessment12
‘mini-liver’ models using scaffold-free bioprinting were developed and used together with label-free nanoscale techniques for the assessment of toxicity of nanodiamond produced by laser-assisted technology and showed improved sensitivity in toxicity assessment.
- A spatially cohesive superpixel model for image noise level estimation12
A spatially cohesive superpixel model is proposed, which can decompose a noisy image into patches that adhere to local structures and hence tend to exhibit increased homogeneity.
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- Histopathology Image Categorization with Discriminative Dimension Reduction of Fisher Vectors12
This paper designs a dimension reduction algorithm in a discriminative learning model with similarity and representation constraints and incorporates two types of local descriptors based on the standard texture feature and unsupervised feature learning to obtain image-level Fisher vectors.
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- Human Connectome Module Pattern Detection Using a New Multi-graph MinMax Cut Model11
A novel multi-graph min-max cut model is introduced to detect the consistent network modules from the brain connectivity networks of all studied subjects and the efficient optimization algorithm is derived.
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- VRM: Knowledge Distillation via Virtual Relation Matching10
This paper revive relational KD by identifying and tackling several key issues in relation-based methods, including their susceptibility to overfitting and spurious responses, and transfers novelly constructed affinity graphs that compactly encapsulate a wealth of beneficial inter-sample, inter-class, and inter-view correlations.
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- Locality constrained encoding of frequency and spatial information for image classification10
The proposed locality-constrained encoding of frequency and spatial information (LEFSI) algorithm, in which an image is decomposed into multiple frequency components and each component is further decomosed into subregions using SPM, is evaluated.
- A load-balancing approach based on modified K-ELM and NSGA-II in a heterogeneous cloud environment10
A new scheme which consists of modified K-ELM and NSGA-II is proposed and results have shown that this method can assign tasks evenly, and effectively improve the performance of a cloud system.
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- ORID: Organ-Regional Information Driven Framework for Radiology Report Generation9
This paper introduces an Organ-Regional Information Driven (ORID) framework which can effectively integrate multi-modal information and reduce the influence of noise from unrelated organs on the radiology report generation.
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- Merged region based image retrieval9
To match the merged regions according to their significance in images, an integrated category matching (ICM) method is designed and experimental results show that the proposed method is more effective than some existing RBIR methods.
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- Ranking-Based Vocabulary Pruning in Bag-of-Features for Image Retrieval9
This study addresses the problem of vocabulary pruning to reduce the influence from the redundant and noisy visual words and proposes a ranking method that can reduce the number of words required while improving the retrieval performance.
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- RealSyn : An Effective and Scalable Multimodal Interleaved Document Transformation Paradigm8
RealSyn is constructed, a dataset combining realistic and synthetic texts, available in three scales: 15M, 30M, and 100M, which significantly enhances contrastive vision-language representation learning and demonstrates robust scalability.
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- NVS-SQA: Exploring Self-Supervised Quality Representation Learning for Neurally Synthesized Scenes Without References7
NVS-SQA is proposed, a NSS quality assessment method to learn no-reference quality representations through self-supervision without reliance on human labels, and shows that NVS-SQA outperforms 17 no-reference methods by a large margin.
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- MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning5
Comparison experiments on two FL MS segmentation scenarios using public and clinical datasets have demonstrated the effectiveness of the proposed method by outperforming other FL methods significantly and indicated the superiority of the method when estimating brain volume differences estimation after lesion inpainting.
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- Beyond benchmarks of IUGC: Rethinking requirements of deep learning method for intrapartum ultrasound biometry from fetal ultrasound videos4
Although promising results have been achieved in the Intrapartum Ultrasound Grand Challenge, the research remains in its early stages, and further in-depth exploration is required before clinical implementation, it is concluded that further in-depth exploration is required before clinical implementation.
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- A Deep Network for Explainable Prediction of Non-imaging Phenotypes Using Anatomical Multi-view Data4
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- How Much Data are Enough? Investigating Dataset Requirements for Patch-Based Brain MRI Segmentation Tasks3
This paper proposes a strategic framework for estimating the amount of annotated data required to train patch-based segmentation networks, and demonstrates that tasks involving regions of interest (ROIs) with different sizes or shapes may yield variably acceptable Dice Similarity Coefficient (DSC) scores.
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- Automatic Segmentation of Thigh Muscle in Longitudinal 3D T1-Weighted Magnetic Resonance (MR) Images2
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- A smart speculative execution strategy based on node classification for heterogeneous Hadoop systems2
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- TEMPORAL AND SPATIAL COMPRESSION OF DYNAMIC POSITRON EMISSION TOMOGRAPHY IN SINOGRAM DOMAIN2
The results of performance evaluation suggest the new compression technique not only is able to reduce the original sinogram datasets by more than 95%, but also improve the reconstructed image quality for the quantitative analysis.
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- Computer-aided lesion detection for brain pet images1
The proposed per-segment method for the automatic detection of lesions in brain PET images based on the asymmetry across segments of the segmented PET data is shown to produce fewer false positives and offer potential advantages in region-of-interest (ROI) extraction for kinetic modelling and quantitative analysis.
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- Beyond Random Masking: A Dual-Stream Approach for Rotation-Invariant Point Cloud Masked Autoencoders–
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- A novel federated learning framework for accurate and secure multi-center MS lesion segmentation–
A decentralized deep learning framework is designed in this work, which can bring accurate multi-center MS lesion segmentation performance without sharing the raw data.
- Multi-Source Unsupervised Domain Adaptation for Neuron Membrane Segmentation via Feature Enhancement–
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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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