{"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/22","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":22,"pages_in_order":51,"rows_per_page":100,"rows":[2101,2200],"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/21","next":"/task/image-segmentation/papers/23","papers":[{"url":null,"slug":"medseg-r-reasoning-segmentation-in-medical","title":"MedSeg-R: Reasoning Segmentation in Medical Images with Multimodal Large Language Models","date":"2025-06-12","arxiv_id":"2506.10465","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-localization-guiding-segment","title":"Semantic Localization Guiding Segment Anything Model For Reference Remote Sensing Image Segmentation","date":"2025-06-12","arxiv_id":"2506.10503","repositories_listed":0,"syntology":null},{"url":null,"slug":"elbo-t2ialign-a-generic-elbo-based-method-for","title":"ELBO-T2IAlign: A Generic ELBO-Based Method for Calibrating Pixel-level Text-Image Alignment in Diffusion Models","date":"2025-06-11","arxiv_id":"2506.09740","repositories_listed":0,"syntology":null},{"url":null,"slug":"contextloss-context-information-for-topology","title":"ContextLoss: Context Information for Topology-Preserving Segmentation","date":"2025-06-10","arxiv_id":"2506.11134","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-system-for-accurate-tracking-and-video","title":"A System for Accurate Tracking and Video Recordings of Rodent Eye Movements using Convolutional Neural Networks for Biomedical Image Segmentation","date":"2025-06-09","arxiv_id":"2506.08183","repositories_listed":0,"syntology":null},{"url":null,"slug":"segment-any-architectural-facades-saaf-an","title":"Segment Any Architectural Facades (SAAF):An automatic segmentation model for building facades, walls and windows based on multimodal semantics guidance","date":"2025-06-09","arxiv_id":"2506.09071","repositories_listed":0,"syntology":null},{"url":null,"slug":"text-guided-multi-stage-cross-perception","title":"Text-guided multi-stage cross-perception network for medical image segmentation","date":"2025-06-09","arxiv_id":"2506.07475","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-contour-models-driven-by-hyperbolic","title":"Active Contour Models Driven by Hyperbolic Mean Curvature Flow for Image Segmentation","date":"2025-06-07","arxiv_id":"2506.06712","repositories_listed":0,"syntology":null},{"url":null,"slug":"2506-05297","title":"DM-SegNet: Dual-Mamba Architecture for 3D Medical Image Segmentation with Global Context Modeling","date":"2025-06-05","arxiv_id":"2506.05297","repositories_listed":0,"syntology":null},{"url":null,"slug":"refer-to-anything-with-vision-language","title":"Refer to Anything with Vision-Language Prompts","date":"2025-06-05","arxiv_id":"2506.05342","repositories_listed":0,"syntology":null},{"url":null,"slug":"sam-aware-test-time-adaptation-for-universal","title":"SAM-aware Test-time Adaptation for Universal Medical Image Segmentation","date":"2025-06-05","arxiv_id":"2506.05221","repositories_listed":0,"syntology":null},{"url":null,"slug":"u-netmn-and-segnetmn-modified-u-net-and","title":"U-NetMN and SegNetMN: Modified U-Net and SegNet models for bimodal SAR image segmentation","date":"2025-06-05","arxiv_id":"2506.05444","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comprehensive-study-on-medical-image","title":"A Comprehensive Study on Medical Image Segmentation using Deep Neural Networks","date":"2025-06-04","arxiv_id":"2506.04121","repositories_listed":0,"syntology":null},{"url":null,"slug":"sounding-that-object-interactive-object-aware","title":"Sounding that Object: Interactive Object-Aware Image to Audio Generation","date":"2025-06-04","arxiv_id":"2506.04214","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-self-prompting-sam-a-prompt-free","title":"Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework","date":"2025-06-03","arxiv_id":"2506.02854","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-pixel-agreement-large-language-models","title":"Beyond Pixel Agreement: Large Language Models as Clinical Guardrails for Reliable Medical Image Segmentation","date":"2025-06-02","arxiv_id":"2506.01841","repositories_listed":0,"syntology":null},{"url":null,"slug":"overcoming-data-scarcity-in-scanning","title":"Overcoming Data Scarcity in Scanning Tunnelling Microscopy Image Segmentation","date":"2025-06-02","arxiv_id":"2506.01678","repositories_listed":0,"syntology":null},{"url":null,"slug":"acm-unet-adaptive-integration-of-cnns-and","title":"ACM-UNet: Adaptive Integration of CNNs and Mamba for Efficient Medical Image Segmentation","date":"2025-05-30","arxiv_id":"2505.24481","repositories_listed":0,"syntology":null},{"url":null,"slug":"soundsculpt-direction-and-semantics-driven","title":"SoundSculpt: Direction and Semantics Driven Ambisonic Target Sound Extraction","date":"2025-05-30","arxiv_id":"2506.00273","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-unsupervised-semantic-segmentation","title":"Federated Unsupervised Semantic Segmentation","date":"2025-05-29","arxiv_id":"2505.23292","repositories_listed":0,"syntology":null},{"url":null,"slug":"pca-for-enhanced-cross-dataset","title":"PCA for Enhanced Cross-Dataset Generalizability in Breast Ultrasound Tumor Segmentation","date":"2025-05-29","arxiv_id":"2505.23587","repositories_listed":0,"syntology":null},{"url":null,"slug":"mambo-net-multi-causal-aware-modeling","title":"MAMBO-NET: Multi-Causal Aware Modeling Backdoor-Intervention Optimization for Medical Image Segmentation Network","date":"2025-05-28","arxiv_id":"2505.21874","repositories_listed":0,"syntology":null},{"url":null,"slug":"sam-r1-leveraging-sam-for-reward-feedback-in","title":"SAM-R1: Leveraging SAM for Reward Feedback in Multimodal Segmentation via Reinforcement Learning","date":"2025-05-28","arxiv_id":"2505.22596","repositories_listed":0,"syntology":null},{"url":null,"slug":"geometric-feature-prompting-of-image","title":"Geometric Feature Prompting of Image Segmentation Models","date":"2025-05-27","arxiv_id":"2505.21644","repositories_listed":0,"syntology":null},{"url":null,"slug":"llamaseg-image-segmentation-via","title":"LlamaSeg: Image Segmentation via Autoregressive Mask Generation","date":"2025-05-26","arxiv_id":"2505.19422","repositories_listed":0,"syntology":null},{"url":null,"slug":"what-you-perceive-is-what-you-conceive-a","title":"What You Perceive Is What You Conceive: A Cognition-Inspired Framework for Open Vocabulary Image Segmentation","date":"2025-05-26","arxiv_id":"2505.19569","repositories_listed":0,"syntology":null},{"url":null,"slug":"deformable-attentive-visual-enhancement-for","title":"Deformable Attentive Visual Enhancement for Referring Segmentation Using Vision-Language Model","date":"2025-05-25","arxiv_id":"2505.19242","repositories_listed":0,"syntology":null},{"url":null,"slug":"frequ-fnet-frequency-aware-u-net-for","title":"FreqU-FNet: Frequency-Aware U-Net for Imbalanced Medical Image Segmentation","date":"2025-05-23","arxiv_id":"2505.17544","repositories_listed":0,"syntology":null},{"url":null,"slug":"auto-nnu-net-towards-automated-medical-image","title":"Auto-nnU-Net: Towards Automated Medical Image Segmentation","date":"2025-05-22","arxiv_id":"2505.16561","repositories_listed":0,"syntology":null},{"url":null,"slug":"baddepth-backdoor-attacks-against-monocular","title":"BadDepth: Backdoor Attacks Against Monocular Depth Estimation in the Physical World","date":"2025-05-22","arxiv_id":"2505.16154","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-prototype-consistency-learning-in","title":"Efficient Prototype Consistency Learning in Medical Image Segmentation via Joint Uncertainty and Data Augmentation","date":"2025-05-22","arxiv_id":"2505.16283","repositories_listed":0,"syntology":null},{"url":null,"slug":"p3net-progressive-and-periodic-perturbation","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","date":"2025-05-21","arxiv_id":"2505.15861","repositories_listed":0,"syntology":null},{"url":null,"slug":"tags-3d-tumor-adaptive-guidance-for-sam","title":"TAGS: 3D Tumor-Adaptive Guidance for SAM","date":"2025-05-21","arxiv_id":"2505.17096","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-gaze-based-volumetric-medical-image","title":"Zero-Shot Gaze-based Volumetric Medical Image Segmentation","date":"2025-05-21","arxiv_id":"2505.15256","repositories_listed":0,"syntology":null},{"url":"/paper/consign-conformal-segmentation-informed-by","slug":"consign-conformal-segmentation-informed-by","title":"CONSIGN: Conformal Segmentation Informed by Spatial Groupings via Decomposition","date":"2025-05-20","arxiv_id":"2505.14113","repositories_listed":0,"syntology":{"n":3,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"0 ran · 3 unverified","sample_list":"/paper/consign-conformal-segmentation-informed-by#ran","syntology_url":"https://syntology.ai/paper/2505.14113","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.14113"}},"official":null}},{"url":null,"slug":"transmedseg-a-transferable-semantic-framework","title":"TransMedSeg: A Transferable Semantic Framework for Semi-Supervised Medical Image Segmentation","date":"2025-05-20","arxiv_id":"2505.14753","repositories_listed":0,"syntology":null},{"url":null,"slug":"unintended-bias-in-2d-image-segmentation-and","title":"Unintended Bias in 2D+ Image Segmentation and Its Effect on Attention Asymmetry","date":"2025-05-20","arxiv_id":"2505.14105","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-learning-for-image","title":"Self-Supervised Learning for Image Segmentation: A Comprehensive Survey","date":"2025-05-19","arxiv_id":"2505.13584","repositories_listed":0,"syntology":null},{"url":null,"slug":"mutual-evidential-deep-learning-for-medical","title":"Mutual Evidential Deep Learning for Medical Image Segmentation","date":"2025-05-18","arxiv_id":"2505.12418","repositories_listed":0,"syntology":null},{"url":null,"slug":"aop-sam-automation-of-prompts-for-efficient","title":"AoP-SAM: Automation of Prompts for Efficient Segmentation","date":"2025-05-17","arxiv_id":"2505.11980","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-deep-learning-approaches-for","title":"Bayesian Deep Learning Approaches for Uncertainty-Aware Retinal OCT Image Segmentation for Multiple Sclerosis","date":"2025-05-17","arxiv_id":"2505.12061","repositories_listed":0,"syntology":null},{"url":null,"slug":"prs-med-position-reasoning-segmentation-with","title":"PRS-Med: Position Reasoning Segmentation with Vision-Language Model in Medical Imaging","date":"2025-05-17","arxiv_id":"2505.11872","repositories_listed":0,"syntology":null},{"url":null,"slug":"2505-11018","title":"Rethinking the Mean Teacher Strategy from the Perspective of Self-paced Learning","date":"2025-05-16","arxiv_id":"2505.11018","repositories_listed":0,"syntology":null},{"url":null,"slug":"boundaryseg-an-embarrassingly-simple-method","title":"BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes","date":"2025-05-14","arxiv_id":"2505.09829","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-foundation-models-as-pseudo-label","title":"Using Foundation Models as Pseudo-Label Generators for Pre-Clinical 4D Cardiac CT Segmentation","date":"2025-05-14","arxiv_id":"2505.09564","repositories_listed":0,"syntology":null},{"url":null,"slug":"calibration-and-uncertainty-for-multirater","title":"Calibration and Uncertainty for multiRater Volume Assessment in multiorgan Segmentation (CURVAS) challenge results","date":"2025-05-13","arxiv_id":"2505.08685","repositories_listed":0,"syntology":null},{"url":null,"slug":"vivit-variable-input-vision-transformer","title":"VIViT: Variable-Input Vision Transformer Framework for 3D MR Image Segmentation","date":"2025-05-13","arxiv_id":"2505.08693","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-kidney-abnormality-segmentation-a","title":"Robust Kidney Abnormality Segmentation: A Validation Study of an AI-Based Framework","date":"2025-05-12","arxiv_id":"2505.07573","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-user-centered-interactive-medical","title":"Towards user-centered interactive medical image segmentation in VR with an assistive AI agent","date":"2025-05-12","arxiv_id":"2505.07214","repositories_listed":0,"syntology":null},{"url":null,"slug":"boosting-cross-spectral-unsupervised-domain","title":"Boosting Cross-spectral Unsupervised Domain Adaptation for Thermal Semantic Segmentation","date":"2025-05-11","arxiv_id":"2505.06951","repositories_listed":0,"syntology":null},{"url":null,"slug":"adapting-a-segmentation-foundation-model-for","title":"Adapting a Segmentation Foundation Model for Medical Image Classification","date":"2025-05-09","arxiv_id":"2505.06217","repositories_listed":0,"syntology":null},{"url":null,"slug":"brainsegdmlf-a-dynamic-fusion-enhanced-sam","title":"BrainSegDMlF: A Dynamic Fusion-enhanced SAM for Brain Lesion Segmentation","date":"2025-05-09","arxiv_id":"2505.06133","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-segmentation-via-variational-model","title":"Image Segmentation via Variational Model Based Tailored UNet: A Deep Variational Framework","date":"2025-05-09","arxiv_id":"2505.05806","repositories_listed":0,"syntology":null},{"url":null,"slug":"structured-prediction-with-abstention-via-the","title":"Structured Prediction with Abstention via the Lovász Hinge","date":"2025-05-09","arxiv_id":"2505.06446","repositories_listed":0,"syntology":null},{"url":null,"slug":"adnp-15-an-open-source-histopathological","title":"ADNP-15: An Open-Source Histopathological Dataset for Neuritic Plaque Segmentation in Human Brain Whole Slide Images with Frequency Domain Image Enhancement for Stain Normalization","date":"2025-05-08","arxiv_id":"2505.05041","repositories_listed":0,"syntology":null},{"url":null,"slug":"advancing-3d-medical-image-segmentation","title":"Advancing 3D Medical Image Segmentation: Unleashing the Potential of Planarian Neural Networks in Artificial Intelligence","date":"2025-05-07","arxiv_id":"2505.04664","repositories_listed":0,"syntology":null},{"url":null,"slug":"raft-robust-augmentation-of-features-for","title":"RAFT: Robust Augmentation of FeaTures for Image Segmentation","date":"2025-05-07","arxiv_id":"2505.04529","repositories_listed":0,"syntology":null},{"url":null,"slug":"artificial-protozoa-optimizer-apo-a-novel-bio","title":"Artificial Protozoa Optimizer (APO): A novel bio-inspired metaheuristic algorithm for engineering optimization","date":"2025-05-06","arxiv_id":"2505.03512","repositories_listed":0,"syntology":null},{"url":null,"slug":"unet-3d-with-adaptive-tverskyce-loss-for","title":"UNet-3D with Adaptive TverskyCE Loss for Pancreas Medical Image Segmentation","date":"2025-05-04","arxiv_id":"2505.01951","repositories_listed":0,"syntology":null},{"url":null,"slug":"not-every-tree-is-a-forest-benchmarking","title":"Not Every Tree Is a Forest: Benchmarking Forest Types from Satellite Remote Sensing","date":"2025-05-03","arxiv_id":"2505.01805","repositories_listed":0,"syntology":null},{"url":null,"slug":"resanything-attribute-prompting-for-arbitrary","title":"RESAnything: Attribute Prompting for Arbitrary Referring Segmentation","date":"2025-05-03","arxiv_id":"2505.02867","repositories_listed":0,"syntology":null},{"url":null,"slug":"gelovec-higher-dimensional-geometric","title":"GeloVec: Higher Dimensional Geometric Smoothing for Coherent Visual Feature Extraction in Image Segmentation","date":"2025-05-02","arxiv_id":"2505.01057","repositories_listed":0,"syntology":null},{"url":null,"slug":"neuroevolution-of-self-attention-over-proto","title":"Neuroevolution of Self-Attention Over Proto-Objects","date":"2025-04-30","arxiv_id":"2505.00186","repositories_listed":0,"syntology":null},{"url":null,"slug":"lymphatlas-a-unified-multimodal-lymphoma","title":"LymphAtlas- A Unified Multimodal Lymphoma Imaging Repository Delivering AI-Enhanced Diagnostic Insight","date":"2025-04-29","arxiv_id":"2504.20454","repositories_listed":0,"syntology":null},{"url":null,"slug":"radsam-segmenting-3d-radiological-images-with","title":"RadSAM: Segmenting 3D radiological images with a 2D promptable model","date":"2025-04-29","arxiv_id":"2504.20837","repositories_listed":0,"syntology":null},{"url":null,"slug":"sam-guided-robust-representation-learning-for","title":"SAM-Guided Robust Representation Learning for One-Shot 3D Medical Image Segmentation","date":"2025-04-29","arxiv_id":"2504.20501","repositories_listed":0,"syntology":null},{"url":null,"slug":"nudf-neural-unsigned-distance-fields-for-high","title":"NUDF: Neural Unsigned Distance Fields for high resolution 3D medical image segmentation","date":"2025-04-25","arxiv_id":"2504.18344","repositories_listed":0,"syntology":null},{"url":null,"slug":"saip-net-enhancing-remote-sensing-image","title":"SAIP-Net: Enhancing Remote Sensing Image Segmentation via Spectral Adaptive Information Propagation","date":"2025-04-23","arxiv_id":"2504.16564","repositories_listed":0,"syntology":null},{"url":null,"slug":"performance-estimation-for-supervised-medical","title":"Performance Estimation for Supervised Medical Image Segmentation Models on Unlabeled Data Using UniverSeg","date":"2025-04-22","arxiv_id":"2504.15667","repositories_listed":0,"syntology":null},{"url":null,"slug":"twig-two-step-image-generation-using","title":"TWIG: Two-Step Image Generation using Segmentation Masks in Diffusion Models","date":"2025-04-21","arxiv_id":"2504.14933","repositories_listed":0,"syntology":null},{"url":null,"slug":"lgd-leveraging-generative-descriptions-for","title":"LGD: Leveraging Generative Descriptions for Zero-Shot Referring Image Segmentation","date":"2025-04-20","arxiv_id":"2504.14467","repositories_listed":0,"syntology":null},{"url":null,"slug":"med-2d-segnet-a-light-weight-deep-neural","title":"Med-2D SegNet: A Light Weight Deep Neural Network for Medical 2D Image Segmentation","date":"2025-04-20","arxiv_id":"2504.14715","repositories_listed":0,"syntology":null},{"url":null,"slug":"supercl-superpixel-guided-contrastive","title":"SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training","date":"2025-04-20","arxiv_id":"2504.14737","repositories_listed":0,"syntology":null},{"url":null,"slug":"wt-bcp-wavelet-transform-based-bidirectional","title":"WT-BCP: Wavelet Transform based Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation","date":"2025-04-20","arxiv_id":"2504.14445","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-modality-guidance-to-enhance-vfm","title":"Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation","date":"2025-04-19","arxiv_id":"2504.14231","repositories_listed":0,"syntology":null},{"url":null,"slug":"lightweight-road-environment-segmentation","title":"Lightweight Road Environment Segmentation using Vector Quantization","date":"2025-04-19","arxiv_id":"2504.14113","repositories_listed":0,"syntology":null},{"url":null,"slug":"dadu-dual-attention-based-deep-supervised","title":"DADU: Dual Attention-based Deep Supervised UNet for Automated Semantic Segmentation of Cardiac Images","date":"2025-04-18","arxiv_id":"2504.13415","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-parameter-adaptation-for-multi","title":"Efficient Parameter Adaptation for Multi-Modal Medical Image Segmentation and Prognosis","date":"2025-04-18","arxiv_id":"2504.13645","repositories_listed":0,"syntology":null},{"url":null,"slug":"contour-field-based-elliptical-shape-prior","title":"Contour Field based Elliptical Shape Prior for the Segment Anything Model","date":"2025-04-17","arxiv_id":"2504.12556","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybrid-dense-unet201-optimization-for-pap","title":"Hybrid Dense-UNet201 Optimization for Pap Smear Image Segmentation Using Spider Monkey Optimization","date":"2025-04-17","arxiv_id":"2504.12807","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-frequency-collaborative-training","title":"Cross-Frequency Collaborative Training Network and Dataset for Semi-supervised First Molar Root Canal Segmentation","date":"2025-04-16","arxiv_id":"2504.11856","repositories_listed":0,"syntology":null},{"url":null,"slug":"remote-sensing-colour-image-semantic","title":"Remote sensing colour image semantic segmentation of trails created by large herbivorous Mammals","date":"2025-04-16","arxiv_id":"2504.12121","repositories_listed":0,"syntology":null},{"url":null,"slug":"textdiffseg-text-guided-latent-diffusion","title":"TextDiffSeg: Text-guided Latent Diffusion Model for 3d Medical Images Segmentation","date":"2025-04-16","arxiv_id":"2504.11825","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-medical-image-restoration-via","title":"Efficient Medical Image Restoration via Reliability Guided Learning in Frequency Domain","date":"2025-04-15","arxiv_id":"2504.11286","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-gaze-to-insight-bridging-human-visual","title":"From Gaze to Insight: Bridging Human Visual Attention and Vision Language Model Explanation for Weakly-Supervised Medical Image Segmentation","date":"2025-04-15","arxiv_id":"2504.11368","repositories_listed":0,"syntology":null},{"url":null,"slug":"lightformer-a-lightweight-and-efficient","title":"LightFormer: A lightweight and efficient decoder for remote sensing image segmentation","date":"2025-04-15","arxiv_id":"2504.10834","repositories_listed":0,"syntology":null},{"url":null,"slug":"hdc-hierarchical-distillation-for-multi-level","title":"HDC: Hierarchical Distillation for Multi-level Noisy Consistency in Semi-Supervised Fetal Ultrasound Segmentation","date":"2025-04-14","arxiv_id":"2504.09876","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-time-seafloor-segmentation-and-mapping","title":"Real-time Seafloor Segmentation and Mapping","date":"2025-04-14","arxiv_id":"2504.10750","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-autonomous-microscopy-for-scalable","title":"Zero-shot Autonomous Microscopy for Scalable and Intelligent Characterization of 2D Materials","date":"2025-04-14","arxiv_id":"2504.10281","repositories_listed":0,"syntology":null},{"url":null,"slug":"mixture-of-shape-experts-mose-end-to-end","title":"Mixture-of-Shape-Experts (MoSE): End-to-End Shape Dictionary Framework to Prompt SAM for Generalizable Medical Segmentation","date":"2025-04-13","arxiv_id":"2504.09601","repositories_listed":0,"syntology":null},{"url":null,"slug":"aeroseg-harnessing-sam-for-open-vocabulary","title":"AerOSeg: Harnessing SAM for Open-Vocabulary Segmentation in Remote Sensing Images","date":"2025-04-12","arxiv_id":"2504.09203","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-modal-brain-tumor-segmentation-via-3d","title":"Multi-Modal Brain Tumor Segmentation via 3D Multi-Scale Self-attention and Cross-attention","date":"2025-04-12","arxiv_id":"2504.09088","repositories_listed":0,"syntology":null},{"url":null,"slug":"pathseqsam-sequential-modeling-for-pathology","title":"PathSeqSAM: Sequential Modeling for Pathology Image Segmentation with SAM2","date":"2025-04-12","arxiv_id":"2504.10526","repositories_listed":0,"syntology":null},{"url":null,"slug":"do-segmentation-models-understand-vascular","title":"Do Segmentation Models Understand Vascular Structure? A Blob-Based XAI Framework","date":"2025-04-11","arxiv_id":"2504.11469","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthfm-training-modality-agnostic-foundation","title":"SynthFM: Training Modality-agnostic Foundation Models for Medical Image Segmentation without Real Medical Data","date":"2025-04-11","arxiv_id":"2504.08177","repositories_listed":0,"syntology":null},{"url":null,"slug":"conditional-conformal-risk-adaptation","title":"Conditional Conformal Risk Adaptation","date":"2025-04-10","arxiv_id":"2504.07611","repositories_listed":0,"syntology":null},{"url":null,"slug":"nonlocal-retinex-based-variational-model-and","title":"Nonlocal Retinex-Based Variational Model and its Deep Unfolding Twin for Low-Light Image Enhancement","date":"2025-04-10","arxiv_id":"2504.07810","repositories_listed":0,"syntology":null},{"url":null,"slug":"prad-periapical-radiograph-analysis-dataset","title":"PRAD: Periapical Radiograph Analysis Dataset and Benchmark Model Development","date":"2025-04-10","arxiv_id":"2504.07760","repositories_listed":0,"syntology":null},{"url":null,"slug":"sydneyscapes-image-segmentation-for","title":"SydneyScapes: Image Segmentation for Australian Environments","date":"2025-04-10","arxiv_id":"2504.07542","repositories_listed":0,"syntology":null},{"url":null,"slug":"zeus-zero-shot-llm-instruction-for-union","title":"Zeus: Zero-shot LLM Instruction for Union Segmentation in Multimodal Medical Imaging","date":"2025-04-09","arxiv_id":"2504.07336","repositories_listed":0,"syntology":null}],"record_sha256":"e1705b0425d1bebcf48b3fbd4d3475c25e3a6380e394c8ec5ed63f51353d04ff","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}