{"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/image-segmentation/papers/35","list_of":"/task/image-segmentation","task":"Image Segmentation","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":35,"pages_in_order":51,"rows_per_page":100,"rows":[3401,3500],"of":5035,"counts":{"archive_papers_tagged":5035,"with_a_code_link":2073,"where_syntology_ran_a_sample":378,"not_listed_spam_title":0,"listed":5035,"listed_where_code_ran":378,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":329,"every_run_a_failure_of_syntologys_instrument":49,"listed_with_a_run_with_no_instrument_failure":329,"listed_every_run_a_failure_of_syntologys_instrument":49,"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/image-segmentation","prev":"/task/image-segmentation/papers/34","next":"/task/image-segmentation/papers/36","papers":[{"url":null,"slug":"ugformer-for-robust-left-atrium-and-scar","title":"UGformer for Robust Left Atrium and Scar Segmentation Across Scanners","date":"2022-10-11","arxiv_id":"2210.05151","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-domain-adaptive-fundus-image-1","title":"Unsupervised Domain Adaptive Fundus Image Segmentation with Few Labeled Source Data","date":"2022-10-10","arxiv_id":"2210.04379","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-fine-grain-segmentation-via","title":"Improving Data-Efficient Fossil Segmentation via Model Editing","date":"2022-10-08","arxiv_id":"2210.03879","repositories_listed":0,"syntology":null},{"url":null,"slug":"topology-preserving-segmentation-network","title":"A Learning-based Framework for Topology-Preserving Segmentation using Quasiconformal Mappings","date":"2022-10-07","arxiv_id":"2210.03299","repositories_listed":0,"syntology":null},{"url":null,"slug":"priornet-lesion-segmentation-in-pet-ct","title":"PriorNet: lesion segmentation in PET-CT including prior tumor appearance information","date":"2022-10-05","arxiv_id":"2210.02203","repositories_listed":0,"syntology":null},{"url":null,"slug":"adawac-adaptively-weighted-augmentation","title":"Adaptively Weighted Data Augmentation Consistency Regularization for Robust Optimization under Concept Shift","date":"2022-10-04","arxiv_id":"2210.01891","repositories_listed":0,"syntology":null},{"url":null,"slug":"wild-animal-classifier-using-cnn","title":"Wild Animal Classifier Using CNN","date":"2022-10-03","arxiv_id":"2210.07973","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-augmented-convnext-unet-for-rectal","title":"Attention Augmented ConvNeXt UNet For Rectal Tumour Segmentation","date":"2022-10-01","arxiv_id":"2210.00227","repositories_listed":0,"syntology":null},{"url":null,"slug":"viewpoint-planning-based-on-shape-completion","title":"NBV-SC: Next Best View Planning based on Shape Completion for Fruit Mapping and Reconstruction","date":"2022-09-30","arxiv_id":"2209.15376","repositories_listed":0,"syntology":null},{"url":null,"slug":"where-should-i-spend-my-flops-efficiency","title":"Where Should I Spend My FLOPS? Efficiency Evaluations of Visual Pre-training Methods","date":"2022-09-30","arxiv_id":"2209.15589","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-satellite-building-construction","title":"Automatic satellite building construction monitoring","date":"2022-09-29","arxiv_id":"2209.15084","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":"grsnet-gated-residual-supervision-network-for","title":"Grsnet: gated residual supervision network for pixel-wise building segmentation in remote sensing imagery","date":"2022-09-23","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"test-test-time-self-training-under","title":"TeST: Test-time Self-Training under Distribution Shift","date":"2022-09-23","arxiv_id":"2209.11459","repositories_listed":0,"syntology":null},{"url":"/paper/segmentation-of-patchy-areas-in-biomedical","slug":"segmentation-of-patchy-areas-in-biomedical","title":"Segmentation of patchy areas in biomedical images based on local edge density estimation","date":"2022-09-22","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-neural-network-and-superpixel-based","title":"Graph Neural Network and Superpixel Based Brain Tissue Segmentation (Corrected Version)","date":"2022-09-21","arxiv_id":"2209.12764","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-segmentation-models-be-trained-with-fully","title":"Can segmentation models be trained with fully synthetically generated data?","date":"2022-09-17","arxiv_id":"2209.08256","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-re-localization-and-history","title":"Contrastive Re-localization and History Distillation in Federated CMR Segmentation","date":"2022-09-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"memory-consistent-unsupervised-off-the-shelf","title":"Memory Consistent Unsupervised Off-the-Shelf Model Adaptation for Source-Relaxed Medical Image Segmentation","date":"2022-09-16","arxiv_id":"2209.07910","repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-medical-image-segmentation-1","title":"Weakly Supervised Medical Image Segmentation With Soft Labels and Noise Robust Loss","date":"2022-09-16","arxiv_id":"2209.08172","repositories_listed":0,"syntology":null},{"url":null,"slug":"medical-image-segmentation-using-levit-unet-a","title":"Medical Image Segmentation using LeViT-UNet++: A Case Study on GI Tract Data","date":"2022-09-15","arxiv_id":"2209.07515","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-evolutionary-computation-for","title":"A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends","date":"2022-09-14","arxiv_id":"2209.06399","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-clustering-method-based-on-information","title":"A Clustering Method Based on Information Entropy Payload","date":"2022-09-13","arxiv_id":"2209.06582","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-resolution-semantically-consistent-image","title":"High-resolution semantically-consistent image-to-image translation","date":"2022-09-13","arxiv_id":"2209.06264","repositories_listed":0,"syntology":null},{"url":null,"slug":"warm-start-active-learning-with-proxy-labels","title":"Warm Start Active Learning with Proxy Labels \\& Selection via Semi-Supervised Fine-Tuning","date":"2022-09-13","arxiv_id":"2209.06285","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-efficient-approach-for-nonconvex","title":"An efficient approach for nonconvex semidefinite optimization via customized alternating direction method of multipliers","date":"2022-09-07","arxiv_id":"2209.03437","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-evaluation-of-u-net-in-renal-structure","title":"An evaluation of U-Net in Renal Structure Segmentation","date":"2022-09-06","arxiv_id":"2209.02247","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-swin-transformer-for-motion","title":"Multi-task Swin Transformer for Motion Artifacts Classification and Cardiac Magnetic Resonance Image Segmentation","date":"2022-09-06","arxiv_id":"2209.02470","repositories_listed":0,"syntology":null},{"url":null,"slug":"source-free-unsupervised-domain-adaptation-2","title":"Source-Free Unsupervised Domain Adaptation with Norm and Shape Constraints for Medical Image Segmentation","date":"2022-09-03","arxiv_id":"2209.01300","repositories_listed":0,"syntology":null},{"url":null,"slug":"mayfly-optimization-with-deep-learning","title":"Mayfly optimization with deep learning enabled retinal fundus image classification model","date":"2022-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/addressing-class-imbalance-in-semi-supervised","slug":"addressing-class-imbalance-in-semi-supervised","title":"Addressing Class Imbalance in Semi-supervised Image Segmentation: A Study on Cardiac MRI","date":"2022-08-31","arxiv_id":"2209.00123","repositories_listed":0,"syntology":null},{"url":null,"slug":"airway-tree-modeling-using-dual-channel-3d","title":"Airway Tree Modeling Using Dual-channel 3D UNet 3+ with Vesselness Prior","date":"2022-08-30","arxiv_id":"2208.13969","repositories_listed":0,"syntology":null},{"url":null,"slug":"parotid-gland-mr-image-segmentation-based-on","title":"Segmentation of Parotid Gland Tumors Using Multimodal MRI and Contrastive Learning","date":"2022-08-26","arxiv_id":"2208.12413","repositories_listed":0,"syntology":null},{"url":null,"slug":"seg4reg-consistency-learning-between-spine","title":"Seg4Reg+: Consistency Learning between Spine Segmentation and Cobb Angle Regression","date":"2022-08-26","arxiv_id":"2208.12462","repositories_listed":0,"syntology":null},{"url":null,"slug":"cats-complementary-cnn-and-transformer","title":"Cats: Complementary CNN and Transformer Encoders for Segmentation","date":"2022-08-24","arxiv_id":"2208.11572","repositories_listed":0,"syntology":null},{"url":null,"slug":"foresteyes-project-conception-enhancements","title":"ForestEyes Project: Conception, Enhancements, and Challenges","date":"2022-08-24","arxiv_id":"2208.11687","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-modality-abdominal-multi-organ","title":"Multi-Modality Abdominal Multi-Organ Segmentation with Deep Supervised 3D Segmentation Model","date":"2022-08-24","arxiv_id":"2208.12041","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-structural-causal-shape-models","title":"Deep Structural Causal Shape Models","date":"2022-08-23","arxiv_id":"2208.10950","repositories_listed":0,"syntology":null},{"url":null,"slug":"split-u-net-preventing-data-leakage-in-split","title":"Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentation","date":"2022-08-22","arxiv_id":"2208.10553","repositories_listed":0,"syntology":null},{"url":null,"slug":"eaa-net-rethinking-the-autoencoder","title":"EAA-Net: Rethinking the Autoencoder Architecture with Intra-class Features for Medical Image Segmentation","date":"2022-08-19","arxiv_id":"2208.09197","repositories_listed":0,"syntology":null},{"url":null,"slug":"low-light-enhancement-method-based-on","title":"Low-light Enhancement Method Based on Attention Map Net","date":"2022-08-19","arxiv_id":"2208.09330","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-dimensional-topological-loss-for","title":"Multi-dimensional topological loss for cortical plate segmentation in fetal brain MRI","date":"2022-08-16","arxiv_id":"2208.07566","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-efficient-multi-scale-fusion-network-for","title":"An Efficient Multi-Scale Fusion Network for 3D Organ at Risk (OAR) Segmentation","date":"2022-08-15","arxiv_id":"2208.07417","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-reconstruction-segmentation-on-graphs","title":"Joint reconstruction-segmentation on graphs","date":"2022-08-11","arxiv_id":"2208.05834","repositories_listed":0,"syntology":null},{"url":null,"slug":"kipa22-report-u-net-with-contour","title":"KiPA22 Report: U-Net with Contour Regularization for Renal Structures Segmentation","date":"2022-08-10","arxiv_id":"2208.05772","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-structure-segmentation-for-renal-cancer","title":"CANet: Channel Extending and Axial Attention Catching Network for Multi-structure Kidney Segmentation","date":"2022-08-10","arxiv_id":"2208.05241","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-ultrasound-image-segmentation-of","title":"Automatic Ultrasound Image Segmentation of Supraclavicular Nerve Using Dilated U-Net Deep Learning Architecture","date":"2022-08-09","arxiv_id":"2208.05050","repositories_listed":0,"syntology":null},{"url":"/paper/tsrformer-table-structure-recognition-with","slug":"tsrformer-table-structure-recognition-with","title":"TSRFormer: Table Structure Recognition with Transformers","date":"2022-08-09","arxiv_id":"2208.04921","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-neuromorphic-approach-to-image-processing","title":"A neuromorphic approach to image processing and machine vision","date":"2022-08-07","arxiv_id":"2209.02595","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-contrastive-learning-for-medical","title":"Distributed Contrastive Learning for Medical Image Segmentation","date":"2022-08-07","arxiv_id":"2208.03808","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-the-effects-of-data-augmentation","title":"Exploring the Effects of Data Augmentation for Drivable Area Segmentation","date":"2022-08-06","arxiv_id":"2208.03437","repositories_listed":0,"syntology":null},{"url":null,"slug":"discover-the-mysteries-of-the-maya-selected","title":"Discover the Mysteries of the Maya: Selected Contributions from the Machine Learning Challenge & The Discovery Challenge Workshop at ECML PKDD 2021","date":"2022-08-05","arxiv_id":"2208.03163","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":"multiclass-asma-vs-targeted-pgd-attack-in","title":"Multiclass ASMA vs Targeted PGD Attack in Image Segmentation","date":"2022-08-03","arxiv_id":"2208.01844","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-robust-morphological-approach-for-semantic","title":"A Robust Morphological Approach for Semantic Segmentation of Very High Resolution Images","date":"2022-08-02","arxiv_id":"2208.01254","repositories_listed":0,"syntology":null},{"url":null,"slug":"lung-nodules-segmentation-from-ct-with","title":"Lung nodules segmentation from CT with DeepHealth toolkit","date":"2022-08-01","arxiv_id":"2208.00641","repositories_listed":0,"syntology":null},{"url":null,"slug":"fixmatchseg-fixing-fixmatch-for-semi","title":"FixMatchSeg: Fixing FixMatch for Semi-Supervised Semantic Segmentation","date":"2022-07-31","arxiv_id":"2208.00400","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-cnns-exploiting-further-inherent-1","title":"Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Image Segmentation","date":"2022-07-29","arxiv_id":"2207.14472","repositories_listed":0,"syntology":null},{"url":null,"slug":"fcsn-global-context-aware-segmentation-by","title":"FCSN: Global Context Aware Segmentation by Learning the Fourier Coefficients of Objects in Medical Images","date":"2022-07-29","arxiv_id":"2207.14477","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-transformer-based-generative-adversarial","title":"A Transformer-based Generative Adversarial Network for Brain Tumor Segmentation","date":"2022-07-28","arxiv_id":"2207.14134","repositories_listed":0,"syntology":null},{"url":null,"slug":"two-stream-unet-networks-for-semantic","title":"Two-Stream UNET Networks for Semantic Segmentation in Medical Images","date":"2022-07-27","arxiv_id":"2207.13337","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-study-on-the-use-of-edge-tpus-for-eye","title":"A Study on the Use of Edge TPUs for Eye Fundus Image Segmentation","date":"2022-07-26","arxiv_id":"2207.12770","repositories_listed":0,"syntology":null},{"url":null,"slug":"flow-2-0-a-flexible-scalable-cross-platform","title":"Flow 2.0 -a flexible, scalable, cross-platform post-processing software for realtime phase contrast sequences","date":"2022-07-26","arxiv_id":"2207.12712","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-based-keypoint-registration-for","title":"Learning-Based Keypoint Registration for Fetoscopic Mosaicking","date":"2022-07-26","arxiv_id":"2207.13185","repositories_listed":0,"syntology":null},{"url":null,"slug":"convunext-an-efficient-convolution-neural","title":"ConvUNeXt: An efficient convolution neural network for medical image segmentation","date":"2022-07-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-better-registration-to-learn-better","title":"Learning Better Registration to Learn Better Few-Shot Medical Image Segmentation: Authenticity, Diversity, and Robustness","date":"2022-07-22","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-aware-multi-modal-learning-via","title":"Uncertainty-aware Multi-modal Learning via Cross-modal Random Network Prediction","date":"2022-07-22","arxiv_id":"2207.10851","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparison-of-automatic-prostate-zones","title":"Comparison of automatic prostate zones segmentation models in MRI images using U-net-like architectures","date":"2022-07-19","arxiv_id":"2207.09483","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-kernel-attention-for-3d-medical-image","title":"Large-Kernel Attention for 3D Medical Image Segmentation","date":"2022-07-19","arxiv_id":"2207.11225","repositories_listed":0,"syntology":null},{"url":null,"slug":"segmentation-of-3d-dental-images-using-deep","title":"Segmentation of 3D Dental Images Using Deep Learning","date":"2022-07-19","arxiv_id":"2207.09582","repositories_listed":0,"syntology":null},{"url":null,"slug":"mlp-gan-for-brain-vessel-image-segmentation","title":"MLP-GAN for Brain Vessel Image Segmentation","date":"2022-07-17","arxiv_id":"2207.08265","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-rcnn-for-medical-image","title":"Self-Supervised-RCNN for Medical Image Segmentation with Limited Data Annotation","date":"2022-07-17","arxiv_id":"2207.11191","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-of-liver-cancer-detection-based-on","title":"Analysis of liver cancer detection based on image processing","date":"2022-07-16","arxiv_id":"2207.08032","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-recognition-of-informative","title":"Unsupervised Recognition of Informative Features via Tensor Network Machine Learning and Quantum Entanglement Variations","date":"2022-07-13","arxiv_id":"2207.06031","repositories_listed":0,"syntology":null},{"url":null,"slug":"forest-and-water-bodies-segmentation-through","title":"Forest and Water Bodies Segmentation Through Satellite Images Using U-Net","date":"2022-07-12","arxiv_id":"2207.11222","repositories_listed":0,"syntology":null},{"url":null,"slug":"pseudoclick-interactive-image-segmentation","title":"PseudoClick: Interactive Image Segmentation with Click Imitation","date":"2022-07-12","arxiv_id":"2207.05282","repositories_listed":0,"syntology":null},{"url":null,"slug":"wound-segmentation-with-dynamic-illumination","title":"Wound Segmentation with Dynamic Illumination Correction and Dual-view Semantic Fusion","date":"2022-07-12","arxiv_id":"2207.05388","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-segmentation-for-radar-signal","title":"Image Segmentation for Radar Signal Deinterleaving Using Deep Learning","date":"2022-07-11","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"vision-transformers-state-of-the-art-and","title":"Vision Transformers: State of the Art and Research Challenges","date":"2022-07-07","arxiv_id":"2207.03041","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-contrast-mri-segmentation-trained-on","title":"Multi-Contrast MRI Segmentation Trained on Synthetic Images","date":"2022-07-06","arxiv_id":"2207.02469","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-label-efficient-deep-segmentation","title":"A Survey on Label-efficient Deep Image Segmentation: Bridging the Gap between Weak Supervision and Dense Prediction","date":"2022-07-04","arxiv_id":"2207.01223","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-reflective-learning-for-robust-medical","title":"Online Reflective Learning for Robust Medical Image Segmentation","date":"2022-07-01","arxiv_id":"2207.00476","repositories_listed":0,"syntology":null},{"url":null,"slug":"segmentation-of-waterbodies-in-remote-sensing","title":"Segmentation of waterbodies in remote sensing images using deep stacked ensemble model","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"implicit-u-net-for-volumetric-medical-image","title":"Implicit U-Net for volumetric medical image segmentation","date":"2022-06-30","arxiv_id":"2206.15217","repositories_listed":0,"syntology":null},{"url":null,"slug":"single-domain-generalization-in-medical-image","title":"Single-domain Generalization in Medical Image Segmentation via Test-time Adaptation from Shape Dictionary","date":"2022-06-29","arxiv_id":"2206.14467","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-consistency-for-single-domain","title":"Adversarial Consistency for Single Domain Generalization in Medical Image Segmentation","date":"2022-06-28","arxiv_id":"2206.13737","repositories_listed":0,"syntology":null},{"url":null,"slug":"maskrange-a-mask-classification-model-for","title":"MaskRange: A Mask-classification Model for Range-view based LiDAR Segmentation","date":"2022-06-24","arxiv_id":"2206.12073","repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-unpaired-multi-modal-medical-image","title":"Toward Unpaired Multi-modal Medical Image Segmentation via Learning Structured Semantic Consistency","date":"2022-06-21","arxiv_id":"2206.10571","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-compatible-learning-for-partially","title":"Deep Compatible Learning for Partially-Supervised Medical Image Segmentation","date":"2022-06-18","arxiv_id":"2206.09148","repositories_listed":0,"syntology":null},{"url":null,"slug":"crisp-reliable-uncertainty-estimation-for","title":"CRISP - Reliable Uncertainty Estimation for Medical Image Segmentation","date":"2022-06-15","arxiv_id":"2206.07664","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-driven-deep-supervision-for-medical","title":"Data-Driven Deep Supervision for Medical Image Segmentation","date":"2022-06-14","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deeprecon-joint-2d-cardiac-segmentation-and","title":"DeepRecon: Joint 2D Cardiac Segmentation and 3D Volume Reconstruction via A Structure-Specific Generative Method","date":"2022-06-14","arxiv_id":"2206.07163","repositories_listed":0,"syntology":null},{"url":null,"slug":"med-danet-dynamic-architecture-network-for","title":"Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation","date":"2022-06-14","arxiv_id":"2206.06575","repositories_listed":0,"syntology":null},{"url":null,"slug":"clamnet-using-contrastive-learning-with","title":"ClamNet: Using contrastive learning with variable depth Unets for medical image segmentation","date":"2022-06-10","arxiv_id":"2206.05225","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-modeling-of-image-and-label-statistics","title":"Joint Modeling of Image and Label Statistics for Enhancing Model Generalizability of Medical Image Segmentation","date":"2022-06-09","arxiv_id":"2206.04336","repositories_listed":0,"syntology":null},{"url":null,"slug":"confuda-contrastive-fewshot-unsupervised","title":"ConFUDA: Contrastive Fewshot Unsupervised Domain Adaptation for Medical Image Segmentation","date":"2022-06-08","arxiv_id":"2206.03888","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-stealing-attack-on-medical-images-is-it","title":"Data Stealing Attack on Medical Images: Is it Safe to Export Networks from Data Lakes?","date":"2022-06-07","arxiv_id":"2206.03391","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-neural-patchworks-coping-with-large","title":"Deep Neural Patchworks: Coping with Large Segmentation Tasks","date":"2022-06-07","arxiv_id":"2206.03210","repositories_listed":0,"syntology":null},{"url":null,"slug":"implementation-of-a-modified-u-net-for","title":"Implementation of a Modified U-Net for Medical Image Segmentation on Edge Devices","date":"2022-06-06","arxiv_id":"2206.02358","repositories_listed":0,"syntology":null},{"url":null,"slug":"masnet-improve-performance-of-siamese","title":"MASNet:Improve Performance of Siamese Networks with Mutual-attention for Remote Sensing Change Detection Tasks","date":"2022-06-06","arxiv_id":"2206.02331","repositories_listed":0,"syntology":null}],"record_sha256":"3945798991238c235b8f0c419d104baa5d9059bb8503af88f53ca8966d494c53","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}