{"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/23","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":23,"pages_in_order":51,"rows_per_page":100,"rows":[2201,2300],"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/22","next":"/task/image-segmentation/papers/24","papers":[{"url":null,"slug":"cti-unet-cascaded-threshold-integration-for","title":"CTI-Unet: Cascaded Threshold Integration for Improved U-Net Segmentation of Pathology Images","date":"2025-04-08","arxiv_id":"2504.05640","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainability-of-ai-uncertainty-application","title":"Explaining Uncertainty in Multiple Sclerosis Lesion Segmentation Beyond Prediction Errors","date":"2025-04-07","arxiv_id":"2504.04814","repositories_listed":0,"syntology":null},{"url":null,"slug":"msa-unet3-multi-scale-attention-unet3-with","title":"MSA-UNet3+: Multi-Scale Attention UNet3+ with New Supervised Prototypical Contrastive Loss for Coronary DSA Image Segmentation","date":"2025-04-07","arxiv_id":"2504.05184","repositories_listed":0,"syntology":null},{"url":null,"slug":"here-comes-the-explanation-a-shapley","title":"Here Comes the Explanation: A Shapley Perspective on Multi-contrast Medical Image Segmentation","date":"2025-04-06","arxiv_id":"2504.04645","repositories_listed":0,"syntology":null},{"url":null,"slug":"performance-analysis-of-deep-learning-models","title":"Performance Analysis of Deep Learning Models for Femur Segmentation in MRI Scan","date":"2025-04-05","arxiv_id":"2504.04066","repositories_listed":0,"syntology":null},{"url":null,"slug":"view2cad-reconstructing-view-centric-cad","title":"View2CAD: Reconstructing View-Centric CAD Models from Single RGB-D Scans","date":"2025-04-05","arxiv_id":"2504.04000","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-encoder-nnu-net-outperforms-transformer","title":"Multi-encoder nnU-Net outperforms Transformer models with self-supervised pretraining","date":"2025-04-04","arxiv_id":"2504.03474","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-granularity-vision-fastformer-with","title":"Multi-Granularity Vision Fastformer with Fusion Mechanism for Skin Lesion Segmentation","date":"2025-04-04","arxiv_id":"2504.03108","repositories_listed":0,"syntology":null},{"url":null,"slug":"agglomerating-large-vision-encoders-via","title":"Agglomerating Large Vision Encoders via Distillation for VFSS Segmentation","date":"2025-04-03","arxiv_id":"2504.02351","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-and-enhancing-segmentation-model","title":"Evaluating and Enhancing Segmentation Model Robustness with Metamorphic Testing","date":"2025-04-03","arxiv_id":"2504.02335","repositories_listed":0,"syntology":null},{"url":null,"slug":"selfmedhpm-self-pre-training-with-hard","title":"SelfMedHPM: Self Pre-training With Hard Patches Mining Masked Autoencoders For Medical Image Segmentation","date":"2025-04-03","arxiv_id":"2504.02524","repositories_listed":0,"syntology":null},{"url":null,"slug":"test-time-adaptation-for-foundation-medical","title":"Test-time Adaptation for Foundation Medical Segmentation Model without Parametric Updates","date":"2025-04-02","arxiv_id":"2504.02008","repositories_listed":0,"syntology":null},{"url":null,"slug":"balancing-multi-target-semi-supervised","title":"Balancing Multi-Target Semi-Supervised Medical Image Segmentation with Collaborative Generalist and Specialists","date":"2025-04-01","arxiv_id":"2504.00862","repositories_listed":0,"syntology":null},{"url":null,"slug":"adapting-vision-foundation-models-for-real","title":"Adapting Vision Foundation Models for Real-time Ultrasound Image Segmentation","date":"2025-03-31","arxiv_id":"2503.24368","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-pretraining-for-aerial-road","title":"Self-Supervised Pretraining for Aerial Road Extraction","date":"2025-03-31","arxiv_id":"2503.24326","repositories_listed":0,"syntology":null},{"url":null,"slug":"waveformer-a-3d-transformer-with-wavelet","title":"WaveFormer: A 3D Transformer with Wavelet-Driven Feature Representation for Efficient Medical Image Segmentation","date":"2025-03-31","arxiv_id":"2503.23764","repositories_listed":0,"syntology":null},{"url":null,"slug":"cadformer-fine-grained-cross-modal-alignment","title":"CADFormer: Fine-Grained Cross-modal Alignment and Decoding Transformer for Referring Remote Sensing Image Segmentation","date":"2025-03-30","arxiv_id":"2503.23456","repositories_listed":0,"syntology":null},{"url":null,"slug":"evolutionary-prompt-optimization-discovers","title":"Evolutionary Prompt Optimization Discovers Emergent Multimodal Reasoning Strategies in Vision-Language Models","date":"2025-03-30","arxiv_id":"2503.23503","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-self-supervised-learning-for-one","title":"Federated Self-Supervised Learning for One-Shot Cross-Modal and Cross-Imaging Technique Segmentation","date":"2025-03-30","arxiv_id":"2503.23507","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-distance-based-metric-for-quality","title":"A Novel Distance-Based Metric for Quality Assessment in Image Segmentation","date":"2025-03-28","arxiv_id":"2504.00023","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-deeplabv3-to-fuse-aerial-and","title":"Enhancing DeepLabV3+ to Fuse Aerial and Satellite Images for Semantic Segmentation","date":"2025-03-28","arxiv_id":"2503.22909","repositories_listed":0,"syntology":null},{"url":null,"slug":"medcl-learning-consistent-anatomy","title":"MedCL: Learning Consistent Anatomy Distribution for Scribble-supervised Medical Image Segmentation","date":"2025-03-28","arxiv_id":"2503.22890","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-domain-generalization-of","title":"Zero-shot Domain Generalization of Foundational Models for 3D Medical Image Segmentation: An Experimental Study","date":"2025-03-28","arxiv_id":"2503.22862","repositories_listed":0,"syntology":null},{"url":null,"slug":"medsegnet10-a-publicly-accessible-network","title":"MedSegNet10: A Publicly Accessible Network Repository for Split Federated Medical Image Segmentation","date":"2025-03-26","arxiv_id":"2503.20830","repositories_listed":0,"syntology":null},{"url":null,"slug":"biprompt-sam-enhancing-image-segmentation-via","title":"BiPrompt-SAM: Enhancing Image Segmentation via Explicit Selection between Point and Text Prompts","date":"2025-03-25","arxiv_id":"2503.19769","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimization-of-medsam-model-based-on","title":"Optimization of MedSAM model based on bounding box adaptive perturbation algorithm","date":"2025-03-25","arxiv_id":"2503.19700","repositories_listed":0,"syntology":null},{"url":null,"slug":"prompt-guided-dual-path-unet-with-mamba-for","title":"Prompt-Guided Dual-Path UNet with Mamba for Medical Image Segmentation","date":"2025-03-25","arxiv_id":"2503.19589","repositories_listed":0,"syntology":null},{"url":null,"slug":"selip-similarity-enhanced-contrastive","title":"SeLIP: Similarity Enhanced Contrastive Language Image Pretraining for Multi-modal Head MRI","date":"2025-03-25","arxiv_id":"2503.19801","repositories_listed":0,"syntology":null},{"url":null,"slug":"show-and-segment-universal-medical-image","title":"Show and Segment: Universal Medical Image Segmentation via In-Context Learning","date":"2025-03-25","arxiv_id":"2503.19359","repositories_listed":0,"syntology":null},{"url":null,"slug":"pso-unet-particle-swarm-optimized-u-net","title":"PSO-UNet: Particle Swarm-Optimized U-Net Framework for Precise Multimodal Brain Tumor Segmentation","date":"2025-03-24","arxiv_id":"2503.19152","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-disease-aware-training-strategy-for","title":"Multi-Disease-Aware Training Strategy for Cardiac MR Image Segmentation","date":"2025-03-23","arxiv_id":"2503.17896","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-attention-diffusion-models-for-zero-shot","title":"Self-Attention Diffusion Models for Zero-Shot Biomedical Image Segmentation: Unlocking New Frontiers in Medical Imaging","date":"2025-03-23","arxiv_id":"2503.18170","repositories_listed":0,"syntology":null},{"url":null,"slug":"topology-preserving-image-segmentation-using","title":"Topology preserving Image segmentation using the iterative convolution-thresholding method","date":"2025-03-22","arxiv_id":"2503.17792","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-attentive-representative-sample-selection","title":"An Attentive Representative Sample Selection Strategy Combined with Balanced Batch Training for Skin Lesion Segmentation","date":"2025-03-21","arxiv_id":"2503.17034","repositories_listed":0,"syntology":null},{"url":null,"slug":"downstream-analysis-of-foundational-medical","title":"Downstream Analysis of Foundational Medical Vision Models for Disease Progression","date":"2025-03-21","arxiv_id":"2503.16842","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-accuracy-pulmonary-vessel-segmentation","title":"High Accuracy Pulmonary Vessel Segmentation for Contrast and Non-contrast CT Images and Clinical Evaluation","date":"2025-03-21","arxiv_id":"2503.16988","repositories_listed":0,"syntology":null},{"url":null,"slug":"mm-unet-meta-mamba-unet-for-medical-image","title":"MM-UNet: Meta Mamba UNet for Medical Image Segmentation","date":"2025-03-21","arxiv_id":"2503.17540","repositories_listed":0,"syntology":null},{"url":null,"slug":"attentional-triple-encoder-network-in","title":"Attentional Triple-Encoder Network in Spatiospectral Domains for Medical Image Segmentation","date":"2025-03-20","arxiv_id":"2503.16389","repositories_listed":0,"syntology":null},{"url":null,"slug":"closer-to-ground-truth-realistic-shape-and","title":"Closer to Ground Truth: Realistic Shape and Appearance Labeled Data Generation for Unsupervised Underwater Image Segmentation","date":"2025-03-20","arxiv_id":"2503.16051","repositories_listed":0,"syntology":null},{"url":null,"slug":"dept-deep-extreme-point-tracing-for","title":"DEPT: Deep Extreme Point Tracing for Ultrasound Image Segmentation","date":"2025-03-19","arxiv_id":"2503.15260","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedsca-federated-tuning-with-similarity","title":"FedSCA: Federated Tuning with Similarity-guided Collaborative Aggregation for Heterogeneous Medical Image Segmentation","date":"2025-03-19","arxiv_id":"2503.15390","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-kan-kan-provides-an-effective","title":"Semi-KAN: KAN Provides an Effective Representation for Semi-Supervised Learning in Medical Image Segmentation","date":"2025-03-19","arxiv_id":"2503.14983","repositories_listed":0,"syntology":null},{"url":null,"slug":"usam-net-a-u-net-based-network-for-improved","title":"USAM-Net: A U-Net-based Network for Improved Stereo Correspondence and Scene Depth Estimation using Features from a Pre-trained Image Segmentation network","date":"2025-03-19","arxiv_id":"2503.14950","repositories_listed":0,"syntology":null},{"url":null,"slug":"boosting-semi-supervised-medical-image","title":"Boosting Semi-Supervised Medical Image Segmentation via Masked Image Consistency and Discrepancy Learning","date":"2025-03-18","arxiv_id":"2503.14013","repositories_listed":0,"syntology":null},{"url":null,"slug":"organ-aware-multi-scale-medical-image","title":"Organ-aware Multi-scale Medical Image Segmentation Using Text Prompt Engineering","date":"2025-03-18","arxiv_id":"2503.13806","repositories_listed":0,"syntology":null},{"url":null,"slug":"romedformer-a-rotary-embedding-transformer","title":"RoMedFormer: A Rotary-Embedding Transformer Foundation Model for 3D Genito-Pelvic Structure Segmentation in MRI and CT","date":"2025-03-18","arxiv_id":"2503.14304","repositories_listed":0,"syntology":null},{"url":null,"slug":"sam2-for-image-and-video-segmentation-a","title":"SAM2 for Image and Video Segmentation: A Comprehensive Survey","date":"2025-03-17","arxiv_id":"2503.12781","repositories_listed":0,"syntology":null},{"url":null,"slug":"sam2-elnet-label-enhancement-and-automatic","title":"SAM2-ELNet: Label Enhancement and Automatic Annotation for Remote Sensing Segmentation","date":"2025-03-16","arxiv_id":"2503.12404","repositories_listed":0,"syntology":null},{"url":null,"slug":"e-sam-training-free-segment-every-entity","title":"E-SAM: Training-Free Segment Every Entity Model","date":"2025-03-15","arxiv_id":"2503.12094","repositories_listed":0,"syntology":null},{"url":null,"slug":"diff-cl-a-novel-cross-pseudo-supervision","title":"Diff-CL: A Novel Cross Pseudo-Supervision Method for Semi-supervised Medical Image Segmentation","date":"2025-03-12","arxiv_id":"2503.09408","repositories_listed":0,"syntology":null},{"url":null,"slug":"gigp-a-global-information-interacting-and","title":"GIGP: A Global Information Interacting and Geometric Priors Focusing Framework for Semi-supervised Medical Image Segmentation","date":"2025-03-12","arxiv_id":"2503.09355","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-medical-imaging-segmentation-on-non","title":"3D Medical Imaging Segmentation on Non-Contrast CT","date":"2025-03-11","arxiv_id":"2503.08361","repositories_listed":0,"syntology":null},{"url":null,"slug":"maskattn-unet-a-mask-attention-driven","title":"MaskAttn-UNet: A Mask Attention-Driven Framework for Universal Low-Resolution Image Segmentation","date":"2025-03-11","arxiv_id":"2503.10686","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-status-of-current-quantum-machine","title":"On the status of current quantum machine learning software","date":"2025-03-11","arxiv_id":"2503.08962","repositories_listed":0,"syntology":null},{"url":null,"slug":"customized-sam-2-for-referring-remote-sensing","title":"Customized SAM 2 for Referring Remote Sensing Image Segmentation","date":"2025-03-10","arxiv_id":"2503.07266","repositories_listed":0,"syntology":null},{"url":null,"slug":"miram-masked-image-reconstruction-across","title":"MIRAM: Masked Image Reconstruction Across Multiple Scales for Breast Lesion Risk Prediction","date":"2025-03-10","arxiv_id":"2503.07157","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantu-net-efficient-wearable-medical-imaging","title":"QuantU-Net: Efficient Wearable Medical Imaging Using Bitwidth as a Trainable Parameter","date":"2025-03-10","arxiv_id":"2503.08719","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-medical-image-segmentation-7","title":"Semi-Supervised Medical Image Segmentation via Knowledge Mining from Large Models","date":"2025-03-10","arxiv_id":"2503.06816","repositories_listed":0,"syntology":null},{"url":null,"slug":"task-specific-knowledge-distillation-from-the","title":"Task-Specific Knowledge Distillation from the Vision Foundation Model for Enhanced Medical Image Segmentation","date":"2025-03-10","arxiv_id":"2503.06976","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-and-text-prompt-segmentation-a-novel","title":"Visual and Text Prompt Segmentation: A Novel Multi-Model Framework for Remote Sensing","date":"2025-03-10","arxiv_id":"2503.07911","repositories_listed":0,"syntology":null},{"url":"/paper/continuous-online-adaptation-driven-by-user","slug":"continuous-online-adaptation-driven-by-user","title":"Continuous Online Adaptation Driven by User Interaction for Medical Image Segmentation","date":"2025-03-09","arxiv_id":"2503.06717","repositories_listed":0,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/continuous-online-adaptation-driven-by-user#ran","syntology_url":"https://syntology.ai/paper/2503.06717","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.06717"}},"official":null}},{"url":null,"slug":"a-label-free-high-precision-residual-moveout","title":"A Label-Free High-Precision Residual Moveout Picking Method for Travel Time Tomography based on Deep Learning","date":"2025-03-08","arxiv_id":"2503.06038","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamically-evolving-segment-anything-model","title":"Dynamically evolving segment anything model with continuous learning for medical image segmentation","date":"2025-03-08","arxiv_id":"2503.06236","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-universal-text-driven-ct-image","title":"Towards Universal Text-driven CT Image Segmentation","date":"2025-03-08","arxiv_id":"2503.06030","repositories_listed":0,"syntology":null},{"url":null,"slug":"partially-supervised-unpaired-multi-modal","title":"Partially Supervised Unpaired Multi-Modal Learning for Label-Efficient Medical Image Segmentation","date":"2025-03-07","arxiv_id":"2503.05190","repositories_listed":0,"syntology":null},{"url":null,"slug":"s4m-segment-anything-with-4-extreme-points","title":"S4M: Segment Anything with 4 Extreme Points","date":"2025-03-07","arxiv_id":"2503.05534","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-sam-with-efficient-prompting-and","title":"Enhancing SAM with Efficient Prompting and Preference Optimization for Semi-supervised Medical Image Segmentation","date":"2025-03-06","arxiv_id":"2503.04639","repositories_listed":0,"syntology":null},{"url":null,"slug":"gencolor-generative-color-concept-association","title":"GenColor: Generative Color-Concept Association in Visual Design","date":"2025-03-05","arxiv_id":"2503.03236","repositories_listed":0,"syntology":null},{"url":null,"slug":"implicit-u-kan2-0-dynamic-efficient-and","title":"Implicit U-KAN2.0: Dynamic, Efficient and Interpretable Medical Image Segmentation","date":"2025-03-05","arxiv_id":"2503.03141","repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-few-shot-medical-image","title":"Rethinking Few-Shot Medical Image Segmentation by SAM2: A Training-Free Framework with Augmentative Prompting and Dynamic Matching","date":"2025-03-05","arxiv_id":"2503.04826","repositories_listed":0,"syntology":null},{"url":null,"slug":"hyperspectral-image-segmentation-with-a","title":"Hyperspectral image segmentation with a machine learning model trained using quantum annealer","date":"2025-03-03","arxiv_id":"2503.01400","repositories_listed":0,"syntology":null},{"url":null,"slug":"primus-enforcing-attention-usage-for-3d","title":"Primus: Enforcing Attention Usage for 3D Medical Image Segmentation","date":"2025-03-03","arxiv_id":"2503.01835","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparsemamba-pcl-scribble-supervised-medical","title":"SparseMamba-PCL: Scribble-Supervised Medical Image Segmentation via SAM-Guided Progressive Collaborative Learning","date":"2025-03-03","arxiv_id":"2503.01633","repositories_listed":0,"syntology":null},{"url":null,"slug":"conformal-lyapunov-optimization-optimal","title":"Conformal Lyapunov Optimization: Optimal Resource Allocation under Deterministic Reliability Constraints","date":"2025-03-01","arxiv_id":"2503.00486","repositories_listed":0,"syntology":null},{"url":null,"slug":"detection-of-customer-interested-garments-in","title":"Detection of Customer Interested Garments in Surveillance Video using Computer Vision","date":"2025-03-01","arxiv_id":"2503.00442","repositories_listed":0,"syntology":null},{"url":null,"slug":"autoregressive-medical-image-segmentation-via","title":"Autoregressive Medical Image Segmentation via Next-Scale Mask Prediction","date":"2025-02-28","arxiv_id":"2502.20784","repositories_listed":0,"syntology":null},{"url":null,"slug":"style-content-decomposition-based-data","title":"Style Content Decomposition-based Data Augmentation for Domain Generalizable Medical Image Segmentation","date":"2025-02-28","arxiv_id":"2502.20619","repositories_listed":0,"syntology":null},{"url":null,"slug":"test-time-modality-generalization-for-medical","title":"Test-Time Modality Generalization for Medical Image Segmentation","date":"2025-02-27","arxiv_id":"2502.19671","repositories_listed":0,"syntology":null},{"url":null,"slug":"nusaaksara-a-multimodal-and-multilingual","title":"NusaAksara: A Multimodal and Multilingual Benchmark for Preserving Indonesian Indigenous Scripts","date":"2025-02-25","arxiv_id":"2502.18148","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-priori-generalizability-estimate-for-a-cnn","title":"A Priori Generalizability Estimate for a CNN","date":"2025-02-24","arxiv_id":"2502.17622","repositories_listed":0,"syntology":null},{"url":null,"slug":"m3da-benchmark-for-unsupervised-domain","title":"M3DA: Benchmark for Unsupervised Domain Adaptation in 3D Medical Image Segmentation","date":"2025-02-24","arxiv_id":"2502.17029","repositories_listed":0,"syntology":null},{"url":null,"slug":"mdn-mamba-driven-dualstream-network-for","title":"MDN: Mamba-Driven Dualstream Network For Medical Hyperspectral Image Segmentation","date":"2025-02-24","arxiv_id":"2502.17255","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparative-review-of-the-histogram-based","title":"A Comparative Tutorial of the Histogram-based Image Segmentation Methods","date":"2025-02-23","arxiv_id":"2502.18550","repositories_listed":0,"syntology":null},{"url":null,"slug":"rewards-based-image-analysis-in-microscopy","title":"Rewards-based image analysis in microscopy","date":"2025-02-23","arxiv_id":"2502.18522","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-translation-based-unsupervised-cross","title":"Image Translation-Based Unsupervised Cross-Modality Domain Adaptation for Medical Image Segmentation","date":"2025-02-21","arxiv_id":"2502.15193","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-u-net-model-and-image","title":"Distributed U-net model and Image Segmentation for Lung Cancer Detection","date":"2025-02-20","arxiv_id":"2502.14928","repositories_listed":0,"syntology":null},{"url":null,"slug":"reinforcement-learning-for-ultrasound-image","title":"Reinforcement Learning for Ultrasound Image Analysis A Comprehensive Review of Advances and Applications","date":"2025-02-20","arxiv_id":"2502.14995","repositories_listed":0,"syntology":null},{"url":null,"slug":"vision-foundation-models-in-medical-image","title":"Vision Foundation Models in Medical Image Analysis: Advances and Challenges","date":"2025-02-20","arxiv_id":"2502.14584","repositories_listed":0,"syntology":null},{"url":null,"slug":"mgfi-net-a-multi-grained-feature-integration","title":"MGFI-Net: A Multi-Grained Feature Integration Network for Enhanced Medical Image Segmentation","date":"2025-02-19","arxiv_id":"2502.13808","repositories_listed":0,"syntology":null},{"url":null,"slug":"wrt-sam-foundation-model-driven-segmentation","title":"WRT-SAM: Foundation Model-Driven Segmentation for Generalized Weld Radiographic Testing","date":"2025-02-17","arxiv_id":"2502.11338","repositories_listed":0,"syntology":null},{"url":null,"slug":"remind-remembering-anatomical-variations-for","title":"RemInD: Remembering Anatomical Variations for Interpretable Domain Adaptive Medical Image Segmentation","date":"2025-02-15","arxiv_id":"2502.10887","repositories_listed":0,"syntology":null},{"url":null,"slug":"instance-segmentation-of-scene-sketches-using","title":"Instance Segmentation of Scene Sketches Using Natural Image Priors","date":"2025-02-13","arxiv_id":"2502.09608","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-lesion-segmentation-in-medical","title":"Improving Lesion Segmentation in Medical Images by Global and Regional Feature Compensation","date":"2025-02-12","arxiv_id":"2502.08675","repositories_listed":0,"syntology":null},{"url":null,"slug":"referring-remote-sensing-image-segmentation","title":"Referring Remote Sensing Image Segmentation via Bidirectional Alignment Guided Joint Prediction","date":"2025-02-12","arxiv_id":"2502.08486","repositories_listed":0,"syntology":null},{"url":null,"slug":"bidirectional-uncertainty-aware-region","title":"Bidirectional Uncertainty-Aware Region Learning for Semi-Supervised Medical Image Segmentation","date":"2025-02-11","arxiv_id":"2502.07457","repositories_listed":0,"syntology":null},{"url":null,"slug":"unpaired-image-to-image-translation-with-3","title":"Unpaired Image-to-Image Translation with Content Preserving Perspective: A Review","date":"2025-02-11","arxiv_id":"2502.08667","repositories_listed":0,"syntology":null},{"url":null,"slug":"fundusam-a-specialized-deep-learning-model","title":"FunduSAM: A Specialized Deep Learning Model for Enhanced Optic Disc and Cup Segmentation in Fundus Images","date":"2025-02-10","arxiv_id":"2502.06220","repositories_listed":0,"syntology":null},{"url":null,"slug":"is-long-range-sequential-modeling-necessary","title":"Is Long Range Sequential Modeling Necessary For Colorectal Tumor Segmentation?","date":"2025-02-10","arxiv_id":"2502.07120","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comprehensive-review-of-u-net-and-its","title":"A Comprehensive Review of U-Net and Its Variants: Advances and Applications in Medical Image Segmentation","date":"2025-02-09","arxiv_id":"2502.06895","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-convolutional-free-method-for-3d","title":"A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation","date":"2025-02-08","arxiv_id":"2502.05396","repositories_listed":0,"syntology":null}],"record_sha256":"1f7d8f5e4b345c3c06eb8b2981cc7595110f26755afd8f170e548155e0753511","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}