{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/medical-image-classification/papers/4","list_of":"/task/medical-image-classification","task":"Medical Image Classification","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":4,"pages_in_order":5,"rows_per_page":100,"rows":[301,400],"of":424,"counts":{"archive_papers_tagged":424,"with_a_code_link":183,"where_syntology_ran_a_sample":32,"not_listed_spam_title":0,"listed":424,"listed_where_code_ran":32,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":29,"every_run_a_failure_of_syntologys_instrument":3,"listed_with_a_run_with_no_instrument_failure":29,"listed_every_run_a_failure_of_syntologys_instrument":3,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/medical-image-classification","prev":"/task/medical-image-classification/papers/3","next":"/task/medical-image-classification/papers/5","papers":[{"url":null,"slug":"class-attention-to-regions-of-lesion-for","title":"Class Attention to Regions of Lesion for Imbalanced Medical Image Recognition","date":"2023-07-19","arxiv_id":"2307.10036","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-from-exemplary-explanations","title":"Learning from Exemplary Explanations","date":"2023-07-12","arxiv_id":"2307.06026","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-whole-pathological-slide-classification","title":"The Whole Pathological Slide Classification via Weakly Supervised Learning","date":"2023-07-12","arxiv_id":"2307.06344","repositories_listed":0,"syntology":null},{"url":null,"slug":"splal-similarity-based-pseudo-labeling-with","title":"SPLAL: Similarity-based pseudo-labeling with alignment loss for semi-supervised medical image classification","date":"2023-07-10","arxiv_id":"2307.04610","repositories_listed":0,"syntology":null},{"url":null,"slug":"invariant-scattering-transform-for-medical-1","title":"Invariant Scattering Transform for Medical Imaging","date":"2023-07-07","arxiv_id":"2307.04771","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-chatgpt-aided-explainable-framework-for","title":"A ChatGPT Aided Explainable Framework for Zero-Shot Medical Image Diagnosis","date":"2023-07-05","arxiv_id":"2307.01981","repositories_listed":0,"syntology":null},{"url":null,"slug":"more-for-less-compact-convolutional","title":"More for Less: Compact Convolutional Transformers Enable Robust Medical Image Classification with Limited Data","date":"2023-07-01","arxiv_id":"2307.00213","repositories_listed":0,"syntology":null},{"url":null,"slug":"vision-through-the-veil-differential-privacy","title":"Vision Through the Veil: Differential Privacy in Federated Learning for Medical Image Classification","date":"2023-06-30","arxiv_id":"2306.17794","repositories_listed":0,"syntology":null},{"url":null,"slug":"label-noise-tolerant-medical-image","title":"Label-noise-tolerant medical image classification via self-attention and self-supervised learning","date":"2023-06-16","arxiv_id":"2306.09718","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-globally-explainable-learning-for","title":"Active Globally Explainable Learning for Medical Images via Class Association Embedding and Cyclic Adversarial Generation","date":"2023-06-12","arxiv_id":"2306.07306","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-the-versatility-of-zero-shot-clip","title":"Exploring the Versatility of Zero-Shot CLIP for Interstitial Lung Disease Classification","date":"2023-06-01","arxiv_id":"2306.01111","repositories_listed":0,"syntology":null},{"url":null,"slug":"forward-forward-contrastive-learning","title":"Forward-Forward Contrastive Learning","date":"2023-05-04","arxiv_id":"2305.02927","repositories_listed":0,"syntology":null},{"url":null,"slug":"revisiting-model-self-interpretability-in-a","title":"A Test Statistic Estimation-based Approach for Establishing Self-interpretable CNN-based Binary Classifiers","date":"2023-03-13","arxiv_id":"2303.06876","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomalnet-outlier-detection-based-malaria","title":"AnoMalNet: Outlier Detection based Malaria Cell Image Classification Method Leveraging Deep Autoencoder","date":"2023-03-10","arxiv_id":"2303.05789","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-federated-learning-for-medical","title":"Optimizing Federated Learning for Medical Image Classification on Distributed Non-iid Datasets with Partial Labels","date":"2023-03-10","arxiv_id":"2303.06180","repositories_listed":0,"syntology":null},{"url":null,"slug":"ppcr-learning-pyramid-pixel-context","title":"Pyramid Pixel Context Adaption Network for Medical Image Classification with Supervised Contrastive Learning","date":"2023-03-03","arxiv_id":"2303.01917","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatio-temporal-structure-consistency-for","title":"Spatio-Temporal Structure Consistency for Semi-supervised Medical Image Classification","date":"2023-03-03","arxiv_id":"2303.01707","repositories_listed":0,"syntology":null},{"url":null,"slug":"cluster-guided-semi-supervised-domain","title":"Cluster-Guided Semi-Supervised Domain Adaptation for Imbalanced Medical Image Classification","date":"2023-03-02","arxiv_id":"2303.01283","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comprehensive-study-of-modern-architectures","title":"A Comprehensive Study of Modern Architectures and Regularization Approaches on CheXpert5000","date":"2023-02-13","arxiv_id":"2302.06684","repositories_listed":0,"syntology":null},{"url":null,"slug":"transfer-learning-and-class-decomposition-for","title":"Transfer Learning and Class Decomposition for Detecting the Cognitive Decline of Alzheimer Disease","date":"2023-01-31","arxiv_id":"2301.13504","repositories_listed":0,"syntology":null},{"url":null,"slug":"convolutional-xgboost-c-xgboost-model-for","title":"Convolutional XGBoost (C-XGBOOST) Model for Brain Tumor Detection","date":"2023-01-05","arxiv_id":"2301.02317","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-new-perspective-to-boost-vision-transformer","title":"A New Perspective to Boost Vision Transformer for Medical Image Classification","date":"2023-01-03","arxiv_id":"2301.00989","repositories_listed":0,"syntology":null},{"url":null,"slug":"pefat-boosting-semi-supervised-medical-image","title":"PEFAT: Boosting Semi-Supervised Medical Image Classification via Pseudo-Loss Estimation and Feature Adversarial Training","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-attacks-and-defences-for-skin","title":"Adversarial Attacks and Defences for Skin Cancer Classification","date":"2022-12-13","arxiv_id":"2212.06822","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-of-explainable-artificial","title":"Analysis of Explainable Artificial Intelligence Methods on Medical Image Classification","date":"2022-12-10","arxiv_id":"2212.10565","repositories_listed":0,"syntology":null},{"url":null,"slug":"vision-transformers-in-medical-imaging-a","title":"Vision Transformers in Medical Imaging: A Review","date":"2022-11-18","arxiv_id":"2211.10043","repositories_listed":0,"syntology":null},{"url":null,"slug":"imbalanced-classification-in-medical-imaging","title":"Imbalanced Classification in Medical Imaging via Regrouping","date":"2022-10-21","arxiv_id":"2210.12234","repositories_listed":0,"syntology":null},{"url":null,"slug":"clinical-targeted-active-learning-for","title":"CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification","date":"2022-10-04","arxiv_id":"2210.01520","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-augmentation-using-feature-generation","title":"Data Augmentation using Feature Generation for Volumetric Medical Images","date":"2022-09-28","arxiv_id":"2209.14097","repositories_listed":0,"syntology":null},{"url":null,"slug":"deeply-supervised-layer-selective-attention","title":"Deeply Supervised Layer Selective Attention Network: Towards Label-Efficient Learning for Medical Image Classification","date":"2022-09-28","arxiv_id":"2209.13844","repositories_listed":0,"syntology":null},{"url":null,"slug":"cec-cnn-a-consecutive-expansion-contraction","title":"CEC-CNN: A Consecutive Expansion-Contraction Convolutional Network for Very Small Resolution Medical Image Classification","date":"2022-09-27","arxiv_id":"2209.13661","repositories_listed":0,"syntology":null},{"url":null,"slug":"single-stage-broad-multi-instance-multi-label","title":"Single-Stage Broad Multi-Instance Multi-Label Learning (BMIML) with Diverse Inter-Correlations and its application to medical image classification","date":"2022-09-06","arxiv_id":"2209.02625","repositories_listed":0,"syntology":null},{"url":null,"slug":"consistency-based-semi-supervised-evidential","title":"Consistency-Based Semi-supervised Evidential Active Learning for Diagnostic Radiograph Classification","date":"2022-09-05","arxiv_id":"2209.01858","repositories_listed":0,"syntology":null},{"url":null,"slug":"openmedia-open-source-medical-image-analysis","title":"OpenMedIA: Open-Source Medical Image Analysis Toolbox and Benchmark under Heterogeneous AI Computing Platforms","date":"2022-08-11","arxiv_id":"2208.05616","repositories_listed":0,"syntology":null},{"url":null,"slug":"radtex-learning-efficient-radiograph","title":"RadTex: Learning Efficient Radiograph Representations from Text Reports","date":"2022-08-05","arxiv_id":"2208.03218","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-automated-classification-and","title":"A Novel Automated Classification and Segmentation for COVID-19 using 3D CT Scans","date":"2022-08-04","arxiv_id":"2208.02910","repositories_listed":0,"syntology":null},{"url":null,"slug":"texture-features-in-medical-image-analysis-a","title":"Texture features in medical image analysis: a survey","date":"2022-08-02","arxiv_id":"2208.02046","repositories_listed":0,"syntology":null},{"url":null,"slug":"information-gain-sampling-for-active-learning","title":"Information Gain Sampling for Active Learning in Medical Image Classification","date":"2022-08-01","arxiv_id":"2208.00974","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-discriminative-representation-via","title":"Learning Discriminative Representation via Metric Learning for Imbalanced Medical Image Classification","date":"2022-07-14","arxiv_id":"2207.06975","repositories_listed":0,"syntology":null},{"url":null,"slug":"pcct-progressive-class-center-triplet-loss","title":"PCCT: Progressive Class-Center Triplet Loss for Imbalanced Medical Image Classification","date":"2022-07-11","arxiv_id":"2207.04793","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-domain-adaptation-using-feature-1","title":"Unsupervised Domain Adaptation Using Feature Disentanglement And GCNs For Medical Image Classification","date":"2022-06-27","arxiv_id":"2206.13123","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-adversarial-learning-strategy-for","title":"A novel adversarial learning strategy for medical image classification","date":"2022-06-23","arxiv_id":"2206.11501","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-reinforced-active-learning-for-multi","title":"Deep reinforced active learning for multi-class image classification","date":"2022-06-20","arxiv_id":"2206.13391","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-importance-of-background-information-for","title":"The Importance of Background Information for Out of Distribution Generalization","date":"2022-06-17","arxiv_id":"2206.08794","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-centroid-supervision-alleviates","title":"Contrastive Centroid Supervision Alleviates Domain Shift in Medical Image Classification","date":"2022-05-31","arxiv_id":"2205.15658","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-pipeline-for-image","title":"Deep learning pipeline for image classification on mobile phones","date":"2022-05-31","arxiv_id":"2206.00105","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-medical-image-classification-from","title":"Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training","date":"2022-05-10","arxiv_id":"2205.04723","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-sample-z-mixup-richer-more-realistic","title":"Multi-Sample $ζ$-mixup: Richer, More Realistic Synthetic Samples from a $p$-Series Interpolant","date":"2022-04-07","arxiv_id":"2204.03323","repositories_listed":0,"syntology":null},{"url":null,"slug":"caipi-in-practice-towards-explainable","title":"CAIPI in Practice: Towards Explainable Interactive Medical Image Classification","date":"2022-04-06","arxiv_id":"2204.02661","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalized-zero-shot-learning-for-medical","title":"Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classification","date":"2022-04-04","arxiv_id":"2204.01728","repositories_listed":0,"syntology":null},{"url":null,"slug":"mix-up-self-supervised-learning-for-contrast","title":"Mix-up Self-Supervised Learning for Contrast-agnostic Applications","date":"2022-04-02","arxiv_id":"2204.00901","repositories_listed":0,"syntology":null},{"url":null,"slug":"when-accuracy-meets-privacy-two-stage","title":"A Two-Stage Federated Transfer Learning Framework in Medical Images Classification on Limited Data: A COVID-19 Case Study","date":"2022-03-24","arxiv_id":"2203.12803","repositories_listed":0,"syntology":null},{"url":null,"slug":"computer-vision-and-machine-learning-for","title":"Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward","date":"2022-03-21","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-ordinal-regression-forest-for-medical","title":"Meta Ordinal Regression Forest for Medical Image Classification with Ordinal Labels","date":"2022-03-15","arxiv_id":"2203.07725","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairprune-achieving-fairness-through-pruning","title":"FairPrune: Achieving Fairness Through Pruning for Dermatological Disease Diagnosis","date":"2022-03-04","arxiv_id":"2203.02110","repositories_listed":0,"syntology":null},{"url":null,"slug":"mutual-attention-based-hybrid-dimensional","title":"Mutual Attention-based Hybrid Dimensional Network for Multimodal Imaging Computer-aided Diagnosis","date":"2022-01-24","arxiv_id":"2201.09421","repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-generative-adversarial","title":"Weakly-supervised Generative Adversarial Networks for medical image classification","date":"2021-11-29","arxiv_id":"2111.14605","repositories_listed":0,"syntology":null},{"url":null,"slug":"medical-knowledge-guided-deep-learning-for","title":"Medical Knowledge-Guided Deep Learning for Imbalanced Medical Image Classification","date":"2021-11-20","arxiv_id":"2111.10620","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-auc-maximization-for-medical-image","title":"Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities","date":"2021-11-01","arxiv_id":"2111.02400","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedsld-federated-learning-with-shared-label","title":"FedSLD: Federated Learning with Shared Label Distribution for Medical Image Classification","date":"2021-10-15","arxiv_id":"2110.08378","repositories_listed":0,"syntology":null},{"url":null,"slug":"compositional-training-for-end-to-end-deep","title":"Compositional Training for End-to-End Deep AUC Maximization","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"does-deep-learning-model-calibration-improve","title":"Does deep learning model calibration improve performance in class-imbalanced medical image classification?","date":"2021-09-29","arxiv_id":"2110.00918","repositories_listed":0,"syntology":null},{"url":null,"slug":"classification-of-covid-19-from-cxr-images-in","title":"Classification of COVID-19 from CXR Images in a 15-class Scenario: an Attempt to Avoid Bias in the System","date":"2021-09-25","arxiv_id":"2109.12453","repositories_listed":0,"syntology":null},{"url":null,"slug":"splitfed-learning-without-client-side","title":"Splitfed learning without client-side synchronization: Analyzing client-side split network portion size to overall performance","date":"2021-09-19","arxiv_id":"2109.09246","repositories_listed":0,"syntology":null},{"url":null,"slug":"privacy-preserving-machine-learning-for","title":"Privacy-preserving Machine Learning for Medical Image Classification","date":"2021-08-29","arxiv_id":"2108.12816","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-transferable-are-self-supervised-features","title":"How Transferable Are Self-supervised Features in Medical Image Classification Tasks?","date":"2021-08-23","arxiv_id":"2108.10048","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-learning-for-medical-image","title":"Semi-supervised learning for medical image classification using imbalanced training data","date":"2021-08-20","arxiv_id":"2108.08956","repositories_listed":0,"syntology":null},{"url":null,"slug":"simple-black-box-universal-adversarial","title":"Simple black-box universal adversarial attacks on medical image classification based on deep neural networks","date":"2021-08-11","arxiv_id":"2108.04979","repositories_listed":0,"syntology":null},{"url":null,"slug":"statistical-dependency-guided-contrastive","title":"Statistical Dependency Guided Contrastive Learning for Multiple Labeling in Prenatal Ultrasound","date":"2021-08-11","arxiv_id":"2108.05055","repositories_listed":0,"syntology":null},{"url":null,"slug":"reconstructing-images-of-two-adjacent-objects","title":"Reconstructing Images of Two Adjacent Objects through Scattering Medium Using Generative Adversarial Network","date":"2021-07-24","arxiv_id":"2107.11574","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-systematic-review-of-transfer-learning","title":"A systematic review of transfer learning based approaches for diabetic retinopathy detection","date":"2021-05-28","arxiv_id":"2105.13793","repositories_listed":0,"syntology":null},{"url":null,"slug":"privacy-preserving-constrained-domain","title":"Privacy-Preserving Constrained Domain Generalization via Gradient Alignment","date":"2021-05-14","arxiv_id":"2105.08511","repositories_listed":0,"syntology":null},{"url":null,"slug":"transfer-learning-for-automatic-brain-tumor","title":"Transfer learning for automatic brain tumor classification Using MRI Images.","date":"2021-03-19","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-knowledge-distillation-for","title":"Variational Knowledge Distillation for Disease Classification in Chest X-Rays","date":"2021-03-19","arxiv_id":"2103.10825","repositories_listed":0,"syntology":null},{"url":null,"slug":"transmed-transformers-advance-multi-modal","title":"TransMed: Transformers Advance Multi-modal Medical Image Classification","date":"2021-03-10","arxiv_id":"2103.05940","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-medical-image-classification-with","title":"Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation","date":"2021-02-28","arxiv_id":"2103.00528","repositories_listed":0,"syntology":null},{"url":null,"slug":"highly-efficient-representation-and-active","title":"Highly Efficient Representation and Active Learning Framework and Its Application to Imbalanced Medical Image Classification","date":"2021-02-25","arxiv_id":"2103.05109","repositories_listed":0,"syntology":null},{"url":null,"slug":"flow-mixup-classifying-multi-labeled-medical","title":"Flow-Mixup: Classifying Multi-labeled Medical Images with Corrupted Labels","date":"2021-02-09","arxiv_id":"2102.08148","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-instance-learning-by-utilizing","title":"Multi-Instance Learning by Utilizing Structural Relationship among Instances","date":"2021-02-03","arxiv_id":"2102.01889","repositories_listed":0,"syntology":null},{"url":"/paper/beyond-fine-tuning-classifying-high","slug":"beyond-fine-tuning-classifying-high","title":"Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations","date":"2021-01-20","arxiv_id":"2101.07945","repositories_listed":0,"syntology":null},{"url":null,"slug":"aggregative-self-supervised-feature-learning","title":"Aggregative Self-Supervised Feature Learning from a Limited Sample","date":"2020-12-14","arxiv_id":"2012.07477","repositories_listed":0,"syntology":null},{"url":null,"slug":"application-of-the-neural-network","title":"Application of the Neural Network Dependability Kit in Real-World Environments","date":"2020-12-14","arxiv_id":"2012.09602","repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-similarity-and-adversarial-learning","title":"Combining Similarity and Adversarial Learning to Generate Visual Explanation: Application to Medical Image Classification","date":"2020-12-14","arxiv_id":"2012.07332","repositories_listed":0,"syntology":null},{"url":null,"slug":"distant-domain-transfer-learning-for-medical","title":"Distant Domain Transfer Learning for Medical Imaging","date":"2020-12-10","arxiv_id":"2012.06346","repositories_listed":0,"syntology":null},{"url":null,"slug":"sag-gan-semi-supervised-attention-guided-gans","title":"SAG-GAN: Semi-Supervised Attention-Guided GANs for Data Augmentation on Medical Images","date":"2020-11-15","arxiv_id":"2011.07534","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-review-of-uncertainty-quantification-in","title":"A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges","date":"2020-11-12","arxiv_id":"2011.06225","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-modal-information-maximization-for","title":"Cross-Modal Information Maximization for Medical Imaging: CMIM","date":"2020-10-20","arxiv_id":"2010.10593","repositories_listed":0,"syntology":null},{"url":null,"slug":"explaining-clinical-decision-support-systems","title":"Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization","date":"2020-10-09","arxiv_id":"2010.05759","repositories_listed":0,"syntology":null},{"url":null,"slug":"med-tex-transferring-and-explaining-knowledge","title":"MED-TEX: Transferring and Explaining Knowledge with Less Data from Pretrained Medical Imaging Models","date":"2020-08-06","arxiv_id":"2008.02593","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-global-spatial-attention-mechanism-in","title":"A Novel Global Spatial Attention Mechanism in Convolutional Neural Network for Medical Image Classification","date":"2020-07-31","arxiv_id":"2007.15897","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-and-exploiting-interclass-visual","title":"Learning and Exploiting Interclass Visual Correlations for Medical Image Classification","date":"2020-07-13","arxiv_id":"2007.06371","repositories_listed":0,"syntology":null},{"url":null,"slug":"4s-dt-self-supervised-super-sample","title":"4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection","date":"2020-06-26","arxiv_id":"2007.11450","repositories_listed":0,"syntology":null},{"url":null,"slug":"4s-dt-self-supervised-super-sample-1","title":"​4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection","date":"2020-06-26","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"interpretable-deep-models-for-cardiac","title":"Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction","date":"2020-06-24","arxiv_id":"2006.13811","repositories_listed":0,"syntology":null},{"url":null,"slug":"hmic-hierarchical-medical-image","title":"HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach","date":"2020-06-12","arxiv_id":"2006.07187","repositories_listed":0,"syntology":null},{"url":null,"slug":"diagnosis-and-analysis-of-celiac-disease-and","title":"Diagnosis and Analysis of Celiac Disease and Environmental Enteropathy on Biopsy Images using Deep Learning Approaches","date":"2020-06-11","arxiv_id":"2006.06627","repositories_listed":0,"syntology":null},{"url":null,"slug":"embedding-task-knowledge-into-3d-neural","title":"Embedding Task Knowledge into 3D Neural Networks via Self-supervised Learning","date":"2020-06-10","arxiv_id":"2006.05798","repositories_listed":0,"syntology":null},{"url":null,"slug":"covid-19-automatic-detection-from-x-ray","title":"Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks","date":"2020-03-25","arxiv_id":"2003.11617","repositories_listed":0,"syntology":null},{"url":null,"slug":"assessing-robustness-to-noise-low-cost-head","title":"Assessing Robustness to Noise: Low-Cost Head CT Triage","date":"2020-03-17","arxiv_id":"2003.07977","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-graphnet-zoo-a-plug-and-play-framework","title":"The GraphNet Zoo: An All-in-One Graph Based Deep Semi-Supervised Framework for Medical Image Classification","date":"2020-03-13","arxiv_id":"2003.06451","repositories_listed":0,"syntology":null}],"record_sha256":"1d9171b9caba787f8703fa6ffd37320db44f46b0c60a0186c75186dc010ace9f","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}