{"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-analysis/papers/11","list_of":"/task/medical-image-analysis","task":"Medical Image Analysis","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":11,"pages_in_order":14,"rows_per_page":100,"rows":[1001,1100],"of":1360,"counts":{"archive_papers_tagged":1360,"with_a_code_link":532,"where_syntology_ran_a_sample":87,"not_listed_spam_title":0,"listed":1360,"listed_where_code_ran":87,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":74,"every_run_a_failure_of_syntologys_instrument":13,"listed_with_a_run_with_no_instrument_failure":74,"listed_every_run_a_failure_of_syntologys_instrument":13,"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-analysis","prev":"/task/medical-image-analysis/papers/10","next":"/task/medical-image-analysis/papers/12","papers":[{"url":null,"slug":"a-unified-3d-framework-for-organs-at-risk","title":"A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning","date":"2022-03-01","arxiv_id":"2203.00624","repositories_listed":0,"syntology":null},{"url":null,"slug":"texture-characterization-of-histopathologic","title":"Texture Characterization of Histopathologic Images Using Ecological Diversity Measures and Discrete Wavelet Transform","date":"2022-02-27","arxiv_id":"2202.13270","repositories_listed":0,"syntology":null},{"url":null,"slug":"implicit-optimizer-for-diffeomorphic-image","title":"Diffeomorphic Image Registration with Neural Velocity Field","date":"2022-02-25","arxiv_id":"2202.12498","repositories_listed":0,"syntology":null},{"url":null,"slug":"transformers-in-medical-image-analysis-a","title":"Transformers in Medical Image Analysis: A Review","date":"2022-02-24","arxiv_id":"2202.12165","repositories_listed":0,"syntology":null},{"url":null,"slug":"augmentation-based-unsupervised-domain","title":"Augmentation based unsupervised domain adaptation","date":"2022-02-23","arxiv_id":"2202.11486","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-objective-dual-simplex-mesh-based","title":"Multi-Objective Dual Simplex-Mesh Based Deformable Image Registration for 3D Medical Images -- Proof of Concept","date":"2022-02-22","arxiv_id":"2202.11001","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-overview-of-deep-learning-in-medical-1","title":"An overview of deep learning in medical imaging","date":"2022-02-17","arxiv_id":"2202.08546","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-neuron-instance-segmentation-based","title":"A General Deep Learning framework for Neuron Instance Segmentation based on Efficient UNet and Morphological Post-processing","date":"2022-02-17","arxiv_id":"2202.08682","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-convolutional-networks-for-multi","title":"Graph Convolutional Networks for Multi-modality Medical Imaging: Methods, Architectures, and Clinical Applications","date":"2022-02-17","arxiv_id":"2202.08916","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-computational-cytology-a","title":"Deep Learning for Computational Cytology: A Survey","date":"2022-02-10","arxiv_id":"2202.05126","repositories_listed":0,"syntology":null},{"url":null,"slug":"aladdin-joint-atlas-building-and","title":"Aladdin: Joint Atlas Building and Diffeomorphic Registration Learning with Pairwise Alignment","date":"2022-02-07","arxiv_id":"2202.03563","repositories_listed":0,"syntology":null},{"url":null,"slug":"mapping-dnn-embedding-manifolds-for-network","title":"Mapping DNN Embedding Manifolds for Network Generalization Prediction","date":"2022-02-03","arxiv_id":"2202.03868","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-training-free-recursive-multiresolution","title":"A training-free recursive multiresolution framework for diffeomorphic deformable image registration","date":"2022-02-01","arxiv_id":"2202.00675","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-review-on-deep-learning-algorithms-for","title":"A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysis","date":"2022-01-28","arxiv_id":"2201.12260","repositories_listed":0,"syntology":null},{"url":null,"slug":"class-aware-generative-adversarial","title":"Class-Aware Adversarial Transformers for Medical Image Segmentation","date":"2022-01-26","arxiv_id":"2201.10737","repositories_listed":0,"syntology":null},{"url":null,"slug":"virtual-adversarial-training-for-semi","title":"Virtual Adversarial Training for Semi-supervised Breast Mass Classification","date":"2022-01-25","arxiv_id":"2201.10675","repositories_listed":0,"syntology":null},{"url":null,"slug":"modality-bank-learn-multi-modality-images","title":"Modality Bank: Learn multi-modality images across data centers without sharing medical data","date":"2022-01-22","arxiv_id":"2201.08955","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-security-of-deep-learning-defences-for","title":"The Security of Deep Learning Defences for Medical Imaging","date":"2022-01-21","arxiv_id":"2201.08661","repositories_listed":0,"syntology":null},{"url":"/paper/myops-a-benchmark-of-myocardial-pathology","slug":"myops-a-benchmark-of-myocardial-pathology","title":"MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images","date":"2022-01-10","arxiv_id":"2201.03186","repositories_listed":0,"syntology":null},{"url":null,"slug":"transfer-learning-for-retinal-vascular","title":"Transfer Learning for Retinal Vascular Disease Detection: A Pilot Study with Diabetic Retinopathy and Retinopathy of Prematurity","date":"2022-01-04","arxiv_id":"2201.01250","repositories_listed":0,"syntology":null},{"url":null,"slug":"cd2-pfed-cyclic-distillation-guided-channel","title":"CD2-pFed: Cyclic Distillation-Guided Channel Decoupling for Model Personalization in Federated Learning","date":"2022-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"intrprt-a-systematic-review-of-and-guidelines","title":"Explainable Medical Imaging AI Needs Human-Centered Design: Guidelines and Evidence from a Systematic Review","date":"2021-12-21","arxiv_id":"2112.12596","repositories_listed":0,"syntology":null},{"url":null,"slug":"performance-or-trust-why-not-both-deep-auc","title":"Performance or Trust? Why Not Both. Deep AUC Maximization with Self-Supervised Learning for COVID-19 Chest X-ray Classifications","date":"2021-12-14","arxiv_id":"2112.08363","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-multi-feature-class-gaussian-process","title":"Dynamic multi feature-class Gaussian process models","date":"2021-12-08","arxiv_id":"2112.04495","repositories_listed":0,"syntology":null},{"url":"/paper/learn2reg-comprehensive-multi-task-medical","slug":"learn2reg-comprehensive-multi-task-medical","title":"Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning","date":"2021-12-08","arxiv_id":"2112.04489","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiscale-softmax-cross-entropy-for-fovea","title":"Multiscale Softmax Cross Entropy for Fovea Localization on Color Fundus Photography","date":"2021-12-08","arxiv_id":"2112.04499","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-human-selective-attention-for","title":"Leveraging Human Selective Attention for Medical Image Analysis with Limited Training Data","date":"2021-12-02","arxiv_id":"2112.01034","repositories_listed":0,"syntology":null},{"url":null,"slug":"feddropoutavg-generalizable-federated","title":"FedDropoutAvg: Generalizable federated learning for histopathology image classification","date":"2021-11-25","arxiv_id":"2111.13230","repositories_listed":0,"syntology":null},{"url":null,"slug":"computer-vision-for-supporting-image-search","title":"Computer Vision for Supporting Image Search","date":"2021-11-16","arxiv_id":"2111.08772","repositories_listed":0,"syntology":null},{"url":null,"slug":"hepatic-vessel-segmentation-based-on-3dswin","title":"Hepatic vessel segmentation based on 3D swin-transformer with inductive biased multi-head self-attention","date":"2021-11-05","arxiv_id":"2111.03368","repositories_listed":0,"syntology":null},{"url":null,"slug":"transparency-of-deep-neural-networks-for","title":"Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods","date":"2021-11-01","arxiv_id":"2111.02398","repositories_listed":0,"syntology":null},{"url":null,"slug":"c-mada-unsupervised-cross-modality","title":"C-MADA: Unsupervised Cross-Modality Adversarial Domain Adaptation framework for medical Image Segmentation","date":"2021-10-29","arxiv_id":"2110.15823","repositories_listed":0,"syntology":null},{"url":null,"slug":"dispensed-transformer-network-for","title":"Dispensed Transformer Network for Unsupervised Domain Adaptation","date":"2021-10-28","arxiv_id":"2110.14944","repositories_listed":0,"syntology":null},{"url":null,"slug":"pl-net-progressive-learning-network-for","title":"PL-Net: Progressive Learning Network for Medical Image Segmentation","date":"2021-10-27","arxiv_id":"2110.14484","repositories_listed":0,"syntology":null},{"url":null,"slug":"patch-vs-global-image-based-unsupervised","title":"Patch vs. Global Image-Based Unsupervised Anomaly Detection in MR Brain Scans of Early Parkinsonian Patients","date":"2021-10-25","arxiv_id":"2110.12707","repositories_listed":0,"syntology":null},{"url":null,"slug":"medusa-multi-scale-encoder-decoder-self","title":"MEDUSA: Multi-scale Encoder-Decoder Self-Attention Deep Neural Network Architecture for Medical Image Analysis","date":"2021-10-12","arxiv_id":"2110.06063","repositories_listed":0,"syntology":null},{"url":null,"slug":"moment-evolution-equations-and-moment","title":"Moment evolution equations and moment matching for stochastic image EPDiff","date":"2021-10-07","arxiv_id":"2110.03337","repositories_listed":0,"syntology":null},{"url":null,"slug":"transferability-estimation-for-semantic","title":"Transferability Estimation for Semantic Segmentation Task","date":"2021-09-30","arxiv_id":"2109.15242","repositories_listed":0,"syntology":null},{"url":null,"slug":"a2b-gan-utilizing-unannotated-anomalous","title":"A2B-GAN: Utilizing Unannotated Anomalous Images for Anomaly Detection in Medical Image Analysis","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"automo-mixer-an-automated-multi-objective","title":"AutoMO-Mixer: An automated multi-objective multi-layer perspecton Mixer model for medical image based diagnosis","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-learning-for-3d-medical-image","title":"Self-Supervised Learning for 3D Medical Image Analysis using 3D SimCLR and Monte Carlo Dropout","date":"2021-09-29","arxiv_id":"2109.14288","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-diffeomorphic-surface","title":"Unsupervised Diffeomorphic Surface Registration and Non-Linear Modelling","date":"2021-09-28","arxiv_id":"2109.13630","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-quantitative-comparison-of-epistemic","title":"A Quantitative Comparison of Epistemic Uncertainty Maps Applied to Multi-Class Segmentation","date":"2021-09-22","arxiv_id":"2109.10702","repositories_listed":0,"syntology":null},{"url":null,"slug":"transformer-unet-raw-image-processing-with","title":"Transformer-Unet: Raw Image Processing with Unet","date":"2021-09-17","arxiv_id":"2109.08417","repositories_listed":0,"syntology":null},{"url":null,"slug":"ssegep-small-segment-emphasized-performance","title":"SSEGEP: Small SEGment Emphasized Performance evaluation metric for medical image segmentation","date":"2021-09-08","arxiv_id":"2109.03435","repositories_listed":0,"syntology":null},{"url":null,"slug":"studying-the-effects-of-self-attention-for","title":"Studying the Effects of Self-Attention for Medical Image Analysis","date":"2021-09-02","arxiv_id":"2109.01486","repositories_listed":0,"syntology":null},{"url":null,"slug":"transformesh-a-transformer-network-for","title":"TransforMesh: A Transformer Network for Longitudinal modeling of Anatomical Meshes","date":"2021-09-01","arxiv_id":"2109.00532","repositories_listed":0,"syntology":null},{"url":null,"slug":"medical-image-segmentation-using-3d","title":"Medical Image Segmentation with 3D Convolutional Neural Networks: A Survey","date":"2021-08-19","arxiv_id":"2108.08467","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-new-bidirectional-unsupervised-domain","title":"A New Bidirectional Unsupervised Domain Adaptation Segmentation Framework","date":"2021-08-18","arxiv_id":"2108.07979","repositories_listed":0,"syntology":null},{"url":null,"slug":"simcvd-simple-contrastive-voxel-wise","title":"SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation","date":"2021-08-13","arxiv_id":"2108.06227","repositories_listed":0,"syntology":null},{"url":null,"slug":"known-operator-learning-and-hybrid-machine","title":"Known Operator Learning and Hybrid Machine Learning in Medical Imaging --- A Review of the Past, the Present, and the Future","date":"2021-08-10","arxiv_id":"2108.04543","repositories_listed":0,"syntology":null},{"url":null,"slug":"deformable-image-registration-using-neural","title":"NODEO: A Neural Ordinary Differential Equation Based Optimization Framework for Deformable Image Registration","date":"2021-08-07","arxiv_id":"2108.03443","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-non-specialists-provide-high-quality-gold","title":"Can non-specialists provide high quality gold standard labels in challenging modalities?","date":"2021-07-30","arxiv_id":"2107.14682","repositories_listed":0,"syntology":null},{"url":null,"slug":"realistic-ultrasound-image-synthesis-for","title":"Realistic Ultrasound Image Synthesis for Improved Classification of Liver Disease","date":"2021-07-27","arxiv_id":"2107.12775","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":"deep-learning-based-cardiac-mri-segmentation","title":"Deep Learning Based Cardiac MRI Segmentation: Do We Need Experts?","date":"2021-07-23","arxiv_id":"2107.11447","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-artificial-intelligence-xai-in","title":"Explainable artificial intelligence (XAI) in deep learning-based medical image analysis","date":"2021-07-22","arxiv_id":"2107.10912","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-privacy-preserving-explanations-in","title":"Towards Privacy-preserving Explanations in Medical Image Analysis","date":"2021-07-20","arxiv_id":"2107.09652","repositories_listed":0,"syntology":null},{"url":null,"slug":"compound-figure-separation-of-biomedical","title":"Compound Figure Separation of Biomedical Images with Side Loss","date":"2021-07-19","arxiv_id":"2107.08650","repositories_listed":0,"syntology":null},{"url":null,"slug":"transductive-image-segmentation-self-training","title":"Transductive image segmentation: Self-training and effect of uncertainty estimation","date":"2021-07-19","arxiv_id":"2107.08964","repositories_listed":0,"syntology":null},{"url":null,"slug":"out-of-distribution-detection-and-adversarial","title":"Out of Distribution Detection and Adversarial Attacks on Deep Neural Networks for Robust Medical Image Analysis","date":"2021-07-10","arxiv_id":"2107.04882","repositories_listed":0,"syntology":null},{"url":null,"slug":"supervised-segmentation-with-domain","title":"Supervised Segmentation with Domain Adaptation for Small Sampled Orbital CT Images","date":"2021-07-01","arxiv_id":"2107.00418","repositories_listed":0,"syntology":null},{"url":null,"slug":"estimating-mri-image-quality-via-image","title":"Estimating MRI Image Quality via Image Reconstruction Uncertainty","date":"2021-06-21","arxiv_id":"2106.10992","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-can-we-learn-more-from-challenges-a","title":"How can we learn (more) from challenges? A statistical approach to driving future algorithm development","date":"2021-06-17","arxiv_id":"2106.09302","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-scale-neural-odes-for-3d-medical-image","title":"Multi-scale Neural ODEs for 3D Medical Image Registration","date":"2021-06-16","arxiv_id":"2106.08493","repositories_listed":0,"syntology":null},{"url":null,"slug":"car-net-unsupervised-co-attention-guided","title":"CAR-Net: Unsupervised Co-Attention Guided Registration Network for Joint Registration and Structure Learning","date":"2021-06-11","arxiv_id":"2106.06637","repositories_listed":0,"syntology":null},{"url":null,"slug":"left-ventricle-contouring-in-cardiac-images","title":"Left Ventricle Contouring in Cardiac Images Based on Deep Reinforcement Learning","date":"2021-06-08","arxiv_id":"2106.04127","repositories_listed":0,"syntology":null},{"url":null,"slug":"pathology-aware-generative-adversarial","title":"Pathology-Aware Generative Adversarial Networks for Medical Image Augmentation","date":"2021-06-03","arxiv_id":"2106.01915","repositories_listed":0,"syntology":null},{"url":null,"slug":"ssmd-semi-supervised-medical-image-detection","title":"SSMD: Semi-Supervised Medical Image Detection with Adaptive Consistency and Heterogeneous Perturbation","date":"2021-06-03","arxiv_id":"2106.01544","repositories_listed":0,"syntology":null},{"url":null,"slug":"recent-advances-and-clinical-applications-of","title":"Recent advances and clinical applications of deep learning in medical image analysis","date":"2021-05-27","arxiv_id":"2105.13381","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-attack-driven-data-augmentation","title":"Adversarial Attack Driven Data Augmentation for Accurate And Robust Medical Image Segmentation","date":"2021-05-25","arxiv_id":"2105.12106","repositories_listed":0,"syntology":null},{"url":null,"slug":"writing-by-memorizing-hierarchical-retrieval","title":"Writing by Memorizing: Hierarchical Retrieval-based Medical Report Generation","date":"2021-05-25","arxiv_id":"2106.06471","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-low-rank-representation-for-unsupervised","title":"A low-rank representation for unsupervised registration of medical images","date":"2021-05-20","arxiv_id":"2105.09548","repositories_listed":0,"syntology":null},{"url":null,"slug":"wide-deep-neural-network-model-for-patch","title":"Wide & Deep neural network model for patch aggregation in CNN-based prostate cancer detection systems","date":"2021-05-20","arxiv_id":"2105.09974","repositories_listed":0,"syntology":null},{"url":null,"slug":"learn-fine-grained-adaptive-loss-for-multiple","title":"Learn Fine-grained Adaptive Loss for Multiple Anatomical Landmark Detection in Medical Images","date":"2021-05-19","arxiv_id":"2105.09124","repositories_listed":0,"syntology":null},{"url":null,"slug":"cuab-convolutional-uncertainty-attention","title":"CUAB: Convolutional Uncertainty Attention Block Enhanced the Chest X-ray Image Analysis","date":"2021-05-05","arxiv_id":"2105.01840","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-artificial-intelligence-for-human","title":"Explainable Artificial Intelligence for Human Decision-Support System in Medical Domain","date":"2021-05-05","arxiv_id":"2105.02357","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-registration-and-segmentation-via-multi","title":"Joint Registration and Segmentation via Multi-Task Learning for Adaptive Radiotherapy of Prostate Cancer","date":"2021-05-05","arxiv_id":"2105.01844","repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-semi-supervised-landmark","title":"Scalable Semi-supervised Landmark Localization for X-ray Images using Few-shot Deep Adaptive Graph","date":"2021-04-29","arxiv_id":"2104.14629","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-rheumatoid-arthritis-joint","title":"Deep Learning for Rheumatoid Arthritis: Joint Detection and Damage Scoring in X-rays","date":"2021-04-28","arxiv_id":"2104.13915","repositories_listed":0,"syntology":null},{"url":null,"slug":"medical-transformer-universal-brain-encoder","title":"Medical Transformer: Universal Brain Encoder for 3D MRI Analysis","date":"2021-04-28","arxiv_id":"2104.13633","repositories_listed":0,"syntology":null},{"url":"/paper/machine-learning-algorithms-for-breast-cancer","slug":"machine-learning-algorithms-for-breast-cancer","title":"Machine Learning Algorithms for Breast Cancer Detection in Mammography Images: A Comparative Study","date":"2021-04-26","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-data-adaptive-loss-function-for-incomplete","title":"A Data-Adaptive Loss Function for Incomplete Data and Incremental Learning in Semantic Image Segmentation","date":"2021-04-22","arxiv_id":"2104.11020","repositories_listed":0,"syntology":null},{"url":null,"slug":"auto-fedavg-learnable-federated-averaging-for","title":"Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation","date":"2021-04-20","arxiv_id":"2104.10195","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpretability-driven-sample-selection","title":"Interpretability-Driven Sample Selection Using Self Supervised Learning For Disease Classification And Segmentation","date":"2021-04-13","arxiv_id":"2104.06087","repositories_listed":0,"syntology":null},{"url":null,"slug":"cascaded-robust-learning-at-imperfect-labels","title":"Cascaded Robust Learning at Imperfect Labels for Chest X-ray Segmentation","date":"2021-04-05","arxiv_id":"2104.01975","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-ensembles-based-on-stochastic-activation","title":"Deep ensembles based on Stochastic Activation Selection for Polyp Segmentation","date":"2021-04-02","arxiv_id":"2104.00850","repositories_listed":0,"syntology":null},{"url":null,"slug":"state-of-the-art-segmentation-network-fooled","title":"Adversarial Heart Attack: Neural Networks Fooled to Segment Heart Symbols in Chest X-Ray Images","date":"2021-03-31","arxiv_id":"2104.00139","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalized-organ-segmentation-by-imitating","title":"Generalized Organ Segmentation by Imitating One-shot Reasoning using Anatomical Correlation","date":"2021-03-30","arxiv_id":"2103.16344","repositories_listed":0,"syntology":null},{"url":null,"slug":"catalyzing-clinical-diagnostic-pipelines","title":"Catalyzing Clinical Diagnostic Pipelines Through Volumetric Medical Image Segmentation Using Deep Neural Networks: Past, Present, & Future","date":"2021-03-27","arxiv_id":"2103.14969","repositories_listed":0,"syntology":null},{"url":null,"slug":"artificial-intelligence-in-tumor-subregion","title":"Artificial Intelligence in Tumor Subregion Analysis Based on Medical Imaging: A Review","date":"2021-03-25","arxiv_id":"2103.13588","repositories_listed":0,"syntology":null},{"url":null,"slug":"channel-scaling-a-scale-and-select-approach","title":"Channel Scaling: A Scale-and-Select Approach for Transfer Learning","date":"2021-03-22","arxiv_id":"2103.12228","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":"cascaded-feature-warping-network-for","title":"Cascaded Feature Warping Network for Unsupervised Medical Image Registration","date":"2021-03-15","arxiv_id":"2103.08213","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-chest-x-ray-analysis-a","title":"Deep Learning for Chest X-ray Analysis: A Survey","date":"2021-03-15","arxiv_id":"2103.08700","repositories_listed":0,"syntology":null},{"url":null,"slug":"principled-ultrasound-data-augmentation-for","title":"Principled Ultrasound Data Augmentation for Classification of Standard Planes","date":"2021-03-14","arxiv_id":"2103.07895","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":"prediction-of-5-year-progression-free","title":"Prediction of 5-year Progression-Free Survival in Advanced Nasopharyngeal Carcinoma with Pretreatment PET/CT using Multi-Modality Deep Learning-based Radiomics","date":"2021-03-09","arxiv_id":"2103.05220","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-reinforcement-learning-in-medical","title":"Deep reinforcement learning in medical imaging: A literature review","date":"2021-03-05","arxiv_id":"2103.05115","repositories_listed":0,"syntology":null},{"url":null,"slug":"feddis-disentangled-federated-learning-for","title":"FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentation","date":"2021-03-05","arxiv_id":"2103.03705","repositories_listed":0,"syntology":null}],"record_sha256":"90f880ca72f7198aa40d86b91989e99a9c2997a7857f4a4c88bd14a8dcc36a9e","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}