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Works386 from public data
- Simple online and realtime tracking4,298
Despite only using a rudimentary combination of familiar techniques such as the Kalman Filter and Hungarian algorithm for the tracking components, this approach achieves an accuracy comparable to state-of-the-art online trackers.
- DeepFruits: A Fruit Detection System Using Deep Neural Networks1,104
A novel multi-modal Faster R-CNN model, which achieves state-of-the-art results compared to prior work with the F1 score, which takes into account both precision and recall performances improving from 0.807 to 0.838 for the detection of sweet pepper.
- Generative OpenMax for Multi-Class Open Set Classification479
The proposed method, called G-OpenMax, extends OpenMax by employing generative adversarial networks (GANs) for novel category image synthesis and provides a way to visualize samples representing the unknown classes from open space.
- Mutual consistency learning for semi-supervised medical image segmentation457
A novel mutual consistency network (MC-Net+) is proposed to effectively exploit the unlabeled data for semi-supervised medical image segmentation to force the model to generate invariant results in such challenging regions, aiming at regularizing the model training.
- Hierarchical Neural Architecture Search for Deep Stereo Matching438
This paper proposes the first end-to-end hierarchical NAS framework for deep stereo matching by incorporating task-specific human knowledge into the neural architecture search framework and optimizing the architectures of the entire pipeline jointly.
- Semi-supervised Left Atrium Segmentation with Mutual Consistency Training363
A novel Mutual Consistency Network (MC-Net) for semi-supervised left atrium segmentation from 3D MR images that consists of one encoder and two slightly different decoders, and the prediction discrepancies of two decoder are transformed as an unsupervised loss by a designed cycled pseudo label scheme to encourage mutual consistency.
- 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.
- Medical visual question answering: A survey245
This survey collects and discusses the publicly available medical VQA datasets up-to-date about the data source, data quantity, and task feature, and summarizes the approaches used in medical V QA tasks.
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- An Interpretable Prediction Model for Identifying N7-Methylguanosine Sites Based on XGBoost and SHAP168
A novel interpretable machine learning-based approach termed XG-m7G for the differentiation of m7G sites using the XGBoost algorithm and six different types of sequence-encoding schemes is developed.
- Artificial Intelligence for Screening of Multiple Retinal and Optic Nerve Diseases157
This diagnostic study develops and prospectively validates a deep learning algorithm that uses ocular fundus images to recognize numerous retinal diseases in a clinical setting at 65 screening centers in 19 Chinese provinces.
- Application of Comprehensive Artificial intelligence Retinal Expert (CARE) system: a national real-world evidence study144
The authors' DLS (CARE) showed satisfactory performance for screening multiple retinal abnormalities in real-world settings using prospectively collected fundus photographs, and so could allow the system to be implemented and adopted for clinical care.
- Skin Disease Recognition Using Deep Saliency Features and Multimodal Learning of Dermoscopy and Clinical Images144
It is demonstrated that the proposed multi-modality method significantly outperforms single- modality methods on three tasks; differentiation between 15 various skin diseases, distinguishing cancerous from non-cancerous moles, and detecting melanoma from benign cases.
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- Vision-Language Navigation with Random Environmental Mixup117
The experimental results on benchmark datasets demonstrate that the augmentation data via REM help the agent reduce its performance gap between the seen and unseen environment and improve the overall performance, making the model the best existing approach on the standard VLN benchmark.
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- Implicit Motion Handling for Video Camouflaged Object Detection111
A new video camouflaged object detection framework that can exploit both short-term dynamics and long-term temporal consistency to detect camouflaged objects from video frames is proposed and unifies motion estimation and object segmentation within a single optimization framework.
- A multimodal vision foundation model for clinical dermatology104
PanDerm, a multimodal dermatology foundation model pretrained through self-supervised learning on over 2 million real-world skin disease images from 11 clinical institutions across 4 imaging modalities, is shown to have strong performance on the full spectrum of clinically relevant tasks and to increase user accuracy in diagnostic tasks.
- EEG datasets for seizure detection and prediction— A review102
This study compiled and compared the different characteristics of the publicly available EEG datasets that are commonly used to develop seizure detection and prediction algorithms, and identified 17 characteristics that make the EEG datasets unique from each other.
- Development and Validation of a Deep Learning Model for Predicting Treatment Response in Patients With Newly Diagnosed Epilepsy99
A deep learning model showed the feasibility of personalized prediction of response to ASMs based on clinical information and may potentially assist clinicians in selecting the right drug at the first trial.
- Healthy dietary patterns and the risk of individual chronic diseases in community-dwelling adults97
Using the UK Biobank, associations of four individual healthy dietary scores with the risk of 48 individual chronic diseases are examined to support dietary guidelines for the prevention of most chronic diseases.
- Big-data and artificial-intelligence-assisted vault prediction and EVO-ICL size selection for myopia correction95
Random Forest, Gradient Boosting and XGBoost models are applicable for vault predicting and ICL sizing and may assist ophthalmologists in improving ICL surgery safety, designing surgical strategies, and predicting clinical outcomes.
- A multimodal visual–language foundation model for computational ophthalmology92
This work proposes EyeCLIP, a multimodal visual-language foundation model trained on 2.77 million ophthalmology images from 11 modalities with partial clinical text, which offers significant potential for detecting both ocular and systemic diseases, and bridging gaps in real-world clinical applications.
- Contextual ensemble network for semantic segmentation91
A novel encoder-decoder architecture, called contextual ensemble network (CENet), for semantic segmentation, where the contextual cues are aggregated via densely usampling the convolutional features of deep layer to the shallow deconvolutional layers.
- Oculomics: Current concepts and evidence87
Oculomics with AI allows prediction of the risk of systemic diseases, enabling risk stratification, opening up new avenues for prevention or individualized risk prediction and prevention, facilitating personalized medicine.
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- Predicting the diagnosis of HIV and sexually transmitted infections among men who have sex with men using machine learning approaches81
Machine learning approaches are advantageous over multivariable logistic regression models in predicting HIV/STIs diagnosis, and GBM models demonstrated the ten greatest predictors collectively explained 62.7-73.6% of variations in predicted HIV/ STIs.
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- FDCNet: filtering deep convolutional network for marine organism classification79
The proposed filtering deep convolutional network (FDCNet) classifies deep-sea objects better than state-of-the-art classification methods, such as AlexNet, GoogLeNet, ResNet50, and ResNet101.
- Computational prediction and interpretation of both general and specific types of promoters in Escherichia coli by exploiting a stacked ensemble-learning framework76
A novel stacked-ensemble approach (termed SELECTOR) for identifying both promoters and their respective classification is presented, which outperformed state-of-the-art methods in both general and specific types of promoter prediction in Escherichia coli.
- Joint Registration And Segmentation Of Xray Images Using Generative Adversarial Networks75
Experiments on chest Xray images show that JRS gives better registration and segmentation performance than when solving them separately.
- Early Melanoma Diagnosis With Sequential Dermoscopic Images74
The proposed model can be used to identify melanocytic lesions that are at high-risk of malignant transformation earlier in the disease process and thereby redefine what is possible in the early detection of melanoma.
- Training data independent image registration using generative adversarial networks and domain adaptation72
This work presents a DL based approach that can perform medical image registration of one image type despite being trained with images of a different type, achieved by unsupervised domain adaptation in the registration process and allows for easier application to different datasets without extensive retraining.
- The Association of Age at Diagnosis of Hypertension With Brain Structure and Incident Dementia in the UK Biobank70
Individuals with hypertension diagnosed at 35 to 44 years of age only had a higher risk of all-cause dementia compared with the corresponding controls after adjustment for covariates, and hypertension diagnosed in early- and mid-life was independently associated with smaller volumes ofgray matter, peripheral cortical gray matter, and white matter.
- A Deep Learning System for Fully Automated Retinal Vessel Measurement in High Throughput Image Analysis67
An artificial intelligence system (Retina-based Microvascular Health Assessment System, RMHAS) for fully automated vessel segmentation and quantification of the retinal microvasculature is developed.
- Structured deep hashing with convolutional neural networks for fast person re-identification67
A novel deep hashing framework with Convolutional Neural Networks (CNNs) for fast person re-identification that simultaneously learns both CNN features and hash functions to get robust yet discriminative features and similarity-preserving hash codes.
- Unsupervised Domain Adaptation for Medical Image Segmentation by Selective Entropy Constraints and Adaptive Semantic Alignment63
An adaptive semantic alignment module is proposed which performs class-level distribution adaptation by minimizing the distance between same class prototypes between domains, where unreliable pixels are removed to derive more accurate prototypes.
- A Machine-Learning-Based Risk-Prediction Tool for HIV and Sexually Transmitted Infections Acquisition over the Next 12 Months62
The risk-prediction tool has acceptable reliability in predicting HIV/STI acquisition over the next 12 months and may be used on clinic websites or digital health platforms to form part of an intervention tool to increase testing or reduce future HIV/ STI risk.
- Fine-grained classification via mixture of deep convolutional neural networks61
The proposed MixDCNN system partitions images into K subsets of similar images and learns an expert DCNN for each subset and combines the output from each of the K DCNNs to form a single classification decision.
- Pseudo-Pair Based Self-Similarity Learning for Unsupervised Person Re-Identification60
Experimental results on several re-ID benchmark datasets demonstrate the superiority of the proposed method over the state-of-the-arts.
- Biomarkers of ageing: Current state‐of‐art, challenges, and opportunities59
A review of currently identified ageing biomarkers in humans is provided, which will provide a comprehensive summary of currently identified ageing biomarkers in humans, discuss the rationale behind each biomarker and highlight their accuracy and clinical value with a contemporary perspective.
- Web-Based Risk Prediction Tool for an Individual's Risk of HIV and Sexually Transmitted Infections Using Machine Learning Algorithms: Development and External Validation Study59
The web-based risk prediction tool, named MySTIRisk, could accurately predict the risk of HIV and STIs for clinic attendees using simple self-reported questions and could serve as an HIV andSTI screening tool on clinic websites or digital health platforms to encourage individuals atrisk of HIV or an STI to be tested or start HIV pre-exposure prophylaxis.
- Association of a wide range of chronic diseases and apolipoprotein E4 genotype with subsequent risk of dementia in community-dwelling adults: A retrospective cohort study59
Individual diseases and multimorbidity are stronger predictors of dementia in non-APOE4 carriers than APOE4, and associations of painful conditions, depression, obesity, diabetes, stroke, Parkinson's disease, high cholesterol, and their multimorbridity with incident dementia were stronger among non- APOE3 carriers.
- Hunting Attributes: Context Prototype-Aware Learning for Weakly Supervised Semantic Segmentation57
This work argues that the knowledge bias between instances and contexts affects the capability of the prototype to sufficiently understand instance semantics, and presents a Context Prototype-Aware Learning (CPAL) strategy, which leverages semantic context to enrich instance comprehension.
- The Importance of Incorporating Human Factors in the Design and Implementation of Artificial Intelligence for Skin Cancer Diagnosis in the Real World57
This review considers human factors and the potential cognitive errors, biases, and unintended consequences that could arise when using an AI skin cancer diagnostic tool in the real world.
- Subset feature learning for fine-grained category classification57
A learning system which first clusters visually similar classes and then learns deep convolutional neural network features specific to each subset of bird classes, yielding improvements in accuracy.
- Relational Subsets Knowledge Distillation for Long-Tailed Retinal Diseases Recognition56
This study proposes class subset learning by dividing the long-tailed data into multiple class subsets according to prior knowledge, such as regions and phenotype information, and enforces the model to focus on learning the subset-specific knowledge.
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- PeNGaRoo, a combined gradient boosting and ensemble learning framework for predicting non-classical secreted proteins55
This work constructed a high-quality dataset of experimentally verified "non-classical" secreted proteins, which was used to create benchmark datasets and developed a two-layer LightGBM ensemble model that integrates several single-feature based models into an overall prediction framework, which achieved a superior performance.
- Adversarial discriminative sim-to-real transfer of visuo-motor policies53
An adversarial discriminative sim-to-real transfer approach to reduce the amount of labeled real data required in visuo-motor policies transferred to real environments, achieving a 97.8% success rate and 1.8 cm control accuracy.
- Modelling local deep convolutional neural network features to improve fine-grained image classification51
A local modelling approach using deep convolutional neural networks for fine-grained image classification by reducing the dimensionality of one of the internal fully connected layers, in conjunction with layer-restricted retraining to avoid retraining the entire network.
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- Improving multi-label chest X-ray disease diagnosis by exploiting disease and health labels dependencies46
This work has proposed a pair of novel error functions that can be employed for any deep learning model, Multi-label Softmax Loss (MSML) and Correlation Loss (CorLoss), to specifically address the properties of multiple labels and visually similar data.
- Medical Image Classification Using Generalized Zero Shot Learning45
A generalized zero shot learning (GZSL) method that uses self supervised learning (SSL) for selecting anchor vectors of different disease classes; and training a feature generator to generate synthetic features of unseen classes is proposed.
- Retinal Abnormalities Recognition Using Regional Multitask Learning43
A novel deep learning based method that can recognise 36 different retinal diseases with a single model is proposed that uses a region-specific multi-task recognition model by learning diseases affecting different regions of the retina with three sub-networks.
- Deep Multiscale Convolutional Feature Learning for Weakly Supervised Localization of Chest Pathologies in X-ray Images43
A novel weakly supervised method to localize chest pathologies using class aware deep multiscale feature learning that leverages intermediate feature maps from CNN layers at different stages of a deep network during the training of a classification model using image level annotations of pathologies.
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- New era of personalised epilepsy management41
The trial and error approach to epilepsy treatment has not changed for over a century but machine learning and patient derived stem cells promise a personalised and more effective strategy.
- Improving Deep Lesion Detection Using 3D Contextual and Spatial Attention41
A dual-attention mechanism to the 3D contextual lesion detection framework is introduced, including the cross-slice contextual attention to selectively aggregate the information from different slices through a soft re-sampling process, and intra-slice spatial attention to focus the feature learning in the most prominent regions.
- Towards Trustable Skin Cancer Diagnosis via Rewriting Model's Decision40
This paper introduces a human-in-the-loop framework in the model training process such that users can observe and correct the model's decision logic when confounding behaviors happen, and demonstrates that the method can automatically discover confounding factors by analyzing the co-occurrence behavior of the samples.
- Generative Metric Learning for Adversarially Robust Open-world Person Re-Identification40
This work improves the robustness of open-world re-ID models by proposing a generative metric learning approach to generate adversarial examples that are regularized to produce robust distance metric.
- Machine learning models for decision support in epilepsy management: A critical review40
The utility of ML models for clinical decision support in epilepsy management remains to be determined and future research should be directed toward conducting larger studies with external validation, standardization of reporting, and prospective evaluation of the ML model on patient outcomes.
- Contrastive pre-training and linear interaction attention-based transformer for universal medical reports generation39
This work proposes a weighted query-key interacting attention module that generates a strong fusion mechanism between language and visual signals and outperformed multiple state-of-the-art image captioning and medical report generation methods on IU X-RAY, MIMIC-CXR, Retina ImBank, and Retina Chinese datasets.
- Differentiable Neural Architecture Search in Equivalent Space with Exploration Enhancement39
A variational graph autoencoder is utilizes to injectively transform the discrete architecture space into an equivalently continuous latent space, to resolve the incongruence.
- Association of a wide range of individual chronic diseases and their multimorbidity with brain volumes in the UK Biobank: A cross-sectional study38
Besides conventional diseases, an association between numerous emerging diseases and smaller brain volumes is found and men or younger adults with multimorbidity are more in need of care for promoting brain health.
- An Intelligent Diagnostic System for Thyroid-Associated Ophthalmopathy Based on Facial Images38
The intelligent diagnostic system introduced in this study accurately identified several common signs of Thyroid-associated ophthalmopathy and accurately diagnosed TAO in three stages.
- Incremental learning for exudate and hemorrhage segmentation on fundus images38
On the Diabetic Retinopathy (DR) lesion segmentation problem, a novel incremental segmentation paradigm is proposed to distill the knowledge of the previous model to improve the current model.
- Few-Shot Learning for Medical Image Segmentation: A Review and Comparative Study36
A review of the literature on few-shot medical image segmentation from 2020 to early 2025, focusing on architectural modifications, loss-inspired learning strategies, and meta-learning frameworks, and foundation models providing in-depth discussions on architectural improvements and representation learning strategies are reviewed.
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- Diversified and Personalized Multi-Rater Medical Image Segmentation34
This work proposes a two-stage framework named D-Persona, which can provide diversified and personalized results at the same time, achieving new SOTA performance for multi-rater medical image segmentation.
- ZSTAD: Zero-Shot Temporal Activity Detection34
This work designs an end-to-end deep network based on R-C3D that is optimized with an innovative loss function that considers the embeddings of activity labels and their super-classes while learning the common semantics of seen and unseen activities.
- Improving Skin cancer Management with ARTificial Intelligence (SMARTI): protocol for a preintervention/postintervention trial of an artificial intelligence system used as a diagnostic aid for skin cancer management in a specialist dermatology setting32
The impact of the AI algorithm on diagnostic and management decisions will be evaluated by comparing the initial management decision of the registrar with their AI-assisted decision and comparing the benign to malignant ratio (for lesions biopsied) between the preintervention and postintervention periods.
- A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers31
A comprehensive, data-centric synthesis that reframes the development of Sci-LLMs as a co-evolution between models and their underlying data substrate, and outlines a paradigm shift toward closed-loop systems where autonomous agents based on Sci-LLMs actively experiment, validate, and contribute to a living, evolving knowledge base.
- A cross population study of retinal aging biomarkers with longitudinal pre-training and label distribution learning31
A deep learning model is developed to estimate retinal age with enhanced accuracy, leveraging retinal images from diverse populations, and integrates self-supervised learning to capture chronological information from both snapshot and sequential images, alongside a progressive label distribution learning module to model biological aging variability.
- Underwater Image High Definition Display Using the Multilayer Perceptron and Color Feature-Based SRCNN31
An image super-resolution reconstruction algorithm that is based on color features is proposed and improves the reconstruction effect of the image edges and texture details, increases the image clarity, and enhances the image color recovery.
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- TP-DRSeg: Improving Diabetic Retinopathy Lesion Segmentation with Explicit Text-Prompts Assisted SAM22
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- Toward Modality Gap: Vision Prototype Learning for Weakly-supervised Semantic Segmentation with CLIP13
- SynFER: Towards Boosting Facial Expression Recognition With Synthetic Data13
This work introduces SynFER (Synthesis of Facial Expressions with Refined Control), a novel synthetic framework for synthesizing facial expression image data based on high-level textual descriptions as well as more fine-grained and precise control through facial action units.
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- 3D Unet-based Kidney and Kidney Tumer Segmentation with Attentive Feature Learning2
An improved deep 3D Unet is introduced by enriching the feature representation in CT images using an attention module and achieves 1.5% improvement in the segmentation accuracy when evaluated on the validation set.
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- Applications of GPT in Cancer for Screening, Diagnosis and Treatment - A Global Comprehensive Review1
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- Multi-Aspect Knowledge-Enhanced Medical Vision-Language Pretraining with Multi-Agent Data Generation–
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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 2026-10-13. 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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