{"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/28","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":28,"pages_in_order":51,"rows_per_page":100,"rows":[2701,2800],"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/27","next":"/task/image-segmentation/papers/29","papers":[{"url":null,"slug":"fuse-calibrate-a-bi-directional-vision","title":"Fuse & Calibrate: A bi-directional Vision-Language Guided Framework for Referring Image Segmentation","date":"2024-05-18","arxiv_id":"2405.11205","repositories_listed":0,"syntology":null},{"url":null,"slug":"haris-human-like-attention-for-reference","title":"HARIS: Human-Like Attention for Reference Image Segmentation","date":"2024-05-17","arxiv_id":"2405.10707","repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-barely-supervised-segmentation","title":"Rethinking Barely-Supervised Volumetric Medical Image Segmentation from an Unsupervised Domain Adaptation Perspective","date":"2024-05-16","arxiv_id":"2405.09777","repositories_listed":0,"syntology":null},{"url":null,"slug":"fourier-boundary-features-network-with-wider","title":"Fourier Boundary Features Network with Wider Catchers for Glass Segmentation","date":"2024-05-15","arxiv_id":"2405.09459","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-from-partial-label-proportions-for","title":"Learning from Partial Label Proportions for Whole Slide Image Segmentation","date":"2024-05-15","arxiv_id":"2405.09041","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatial-semantic-recurrent-mining-for","title":"Spatial Semantic Recurrent Mining for Referring Image Segmentation","date":"2024-05-15","arxiv_id":"2405.09006","repositories_listed":0,"syntology":null},{"url":null,"slug":"palette-based-color-transfer-between-images","title":"Palette-based Color Transfer between Images","date":"2024-05-14","arxiv_id":"2405.08263","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-clinician-preferred-segmentation","title":"Towards Clinician-Preferred Segmentation: Leveraging Human-in-the-Loop for Test Time Adaptation in Medical Image Segmentation","date":"2024-05-14","arxiv_id":"2405.08270","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-method-for-supervoxel-wise-association","title":"A method for supervoxel-wise association studies of age and other non-imaging variables from coronary computed tomography angiograms","date":"2024-05-13","arxiv_id":"2405.07762","repositories_listed":0,"syntology":null},{"url":null,"slug":"support-query-prototype-fusion-network-for","title":"Support-Query Prototype Fusion Network for Few-shot Medical Image Segmentation","date":"2024-05-13","arxiv_id":"2405.07516","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-fixed-and-dynamic-pseudo-labels","title":"Leveraging Fixed and Dynamic Pseudo-labels for Semi-supervised Medical Image Segmentation","date":"2024-05-12","arxiv_id":"2405.07256","repositories_listed":0,"syntology":null},{"url":null,"slug":"adlda-a-method-to-reduce-the-harm-of-data","title":"ADLDA: A Method to Reduce the Harm of Data Distribution Shift in Data Augmentation","date":"2024-05-11","arxiv_id":"2405.06893","repositories_listed":0,"syntology":null},{"url":null,"slug":"hc-mamba-vision-mamba-with-hybrid","title":"HC-Mamba: Vision MAMBA with Hybrid Convolutional Techniques for Medical Image Segmentation","date":"2024-05-08","arxiv_id":"2405.05007","repositories_listed":0,"syntology":null},{"url":null,"slug":"salfau-net-saliency-fusion-attention-u-net","title":"SalFAU-Net: Saliency Fusion Attention U-Net for Salient Object Detection","date":"2024-05-05","arxiv_id":"2405.02906","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-risk-control-a-generalized-framework-for","title":"Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Risks","date":"2024-05-03","arxiv_id":"2405.02225","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-learning-enabled-low-cost-cell-image","title":"Active Learning Enabled Low-cost Cell Image Segmentation Using Bounding Box Annotation","date":"2024-05-02","arxiv_id":"2405.01701","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-virtual-product-placement-and","title":"Automated Virtual Product Placement and Assessment in Images using Diffusion Models","date":"2024-05-02","arxiv_id":"2405.01130","repositories_listed":0,"syntology":null},{"url":"/paper/cromss-cross-modal-pre-training-with-noisy","slug":"cromss-cross-modal-pre-training-with-noisy","title":"CromSS: Cross-modal pre-training with noisy labels for remote sensing image segmentation","date":"2024-05-02","arxiv_id":"2405.01217","repositories_listed":0,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/cromss-cross-modal-pre-training-with-noisy#ran","syntology_url":"https://syntology.ai/paper/2405.01217","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.01217"}},"official":null}},{"url":null,"slug":"explainable-ai-xai-in-image-segmentation-in","title":"Explainable AI (XAI) in Image Segmentation in Medicine, Industry, and Beyond: A Survey","date":"2024-05-02","arxiv_id":"2405.01636","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-segmentation-of-treated-and-untreated","title":"Image segmentation of treated and untreated tumor spheroids by Fully Convolutional Networks","date":"2024-05-02","arxiv_id":"2405.01105","repositories_listed":0,"syntology":null},{"url":null,"slug":"dmads-net-dense-multiscale-attention-and","title":"DmADs-Net: Dense multiscale attention and depth-supervised network for medical image segmentation","date":"2024-05-01","arxiv_id":"2405.00472","repositories_listed":0,"syntology":null},{"url":null,"slug":"specstator-speckle-statistics-based-ioct","title":"SpecstatOR: Speckle statistics-based iOCT Segmentation Network for Ophthalmic Surgery","date":"2024-04-30","arxiv_id":"2404.19481","repositories_listed":0,"syntology":null},{"url":null,"slug":"clicks2line-using-lines-for-interactive-image","title":"Clicks2Line: Using Lines for Interactive Image Segmentation","date":"2024-04-29","arxiv_id":"2404.18461","repositories_listed":0,"syntology":null},{"url":null,"slug":"u-nets-as-belief-propagation-efficient","title":"U-Nets as Belief Propagation: Efficient Classification, Denoising, and Diffusion in Generative Hierarchical Models","date":"2024-04-29","arxiv_id":"2404.18444","repositories_listed":0,"syntology":null},{"url":null,"slug":"segmentation-quality-and-volumetric-accuracy","title":"Segmentation Quality and Volumetric Accuracy in Medical Imaging","date":"2024-04-27","arxiv_id":"2404.17742","repositories_listed":0,"syntology":null},{"url":null,"slug":"diffseg-a-segmentation-model-for-skin-lesions","title":"DiffSeg: A Segmentation Model for Skin Lesions Based on Diffusion Difference","date":"2024-04-25","arxiv_id":"2404.16474","repositories_listed":0,"syntology":null},{"url":null,"slug":"discriminative-features-pyramid-network-for-2","title":"Discriminative features pyramid network for medical image segmentation","date":"2024-04-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-view-cardiac-image-segmentation-via","title":"Multi-view Cardiac Image Segmentation via Trans-Dimensional Priors","date":"2024-04-25","arxiv_id":"2404.16708","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-segmentation-refiner-for-ultrasound","title":"Semantic Segmentation Refiner for Ultrasound Applications with Zero-Shot Foundation Models","date":"2024-04-25","arxiv_id":"2404.16325","repositories_listed":0,"syntology":null},{"url":null,"slug":"autogluon-multimodal-automm-supercharging","title":"AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models","date":"2024-04-24","arxiv_id":"2404.16233","repositories_listed":0,"syntology":null},{"url":null,"slug":"does-sam-dream-of-eig-characterizing","title":"Does SAM dream of EIG? Characterizing Interactive Segmenter Performance using Expected Information Gain","date":"2024-04-24","arxiv_id":"2404.16155","repositories_listed":0,"syntology":null},{"url":null,"slug":"mitigating-false-predictions-in-unreasonable","title":"Mitigating False Predictions In Unreasonable Body Regions","date":"2024-04-24","arxiv_id":"2404.15718","repositories_listed":0,"syntology":null},{"url":null,"slug":"cfpformer-feature-pyramid-like-transformer","title":"CFPFormer: Feature-pyramid like Transformer Decoder for Segmentation and Detection","date":"2024-04-23","arxiv_id":"2404.15451","repositories_listed":0,"syntology":null},{"url":null,"slug":"pemma-parameter-efficient-multi-modal","title":"PEMMA: Parameter-Efficient Multi-Modal Adaptation for Medical Image Segmentation","date":"2024-04-21","arxiv_id":"2404.13704","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-pixel-wise-supervision-for-medical","title":"Beyond Pixel-Wise Supervision for Medical Image Segmentation: From Traditional Models to Foundation Models","date":"2024-04-20","arxiv_id":"2404.13239","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-cranial-defect-reconstruction-with","title":"Automatic Cranial Defect Reconstruction with Self-Supervised Deep Deformable Masked Autoencoders","date":"2024-04-19","arxiv_id":"2404.13106","repositories_listed":0,"syntology":null},{"url":null,"slug":"elev-vision-sam-integrated-vision-language","title":"ELEV-VISION-SAM: Integrated Vision Language and Foundation Model for Automated Estimation of Building Lowest Floor Elevation","date":"2024-04-19","arxiv_id":"2404.12606","repositories_listed":0,"syntology":null},{"url":null,"slug":"esc-evolutionary-stitched-camera-calibration","title":"ESC: Evolutionary Stitched Camera Calibration in the Wild","date":"2024-04-19","arxiv_id":"2404.12694","repositories_listed":0,"syntology":null},{"url":null,"slug":"foundation-model-assisted-weakly-supervised-1","title":"Weakly Supervised LiDAR Semantic Segmentation via Scatter Image Annotation","date":"2024-04-19","arxiv_id":"2404.12861","repositories_listed":0,"syntology":null},{"url":null,"slug":"tonno-tomographic-reconstruction-of-a-neural","title":"ToNNO: Tomographic Reconstruction of a Neural Network's Output for Weakly Supervised Segmentation of 3D Medical Images","date":"2024-04-19","arxiv_id":"2404.13103","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-model-mutual-learning-for-exemplar","title":"Cross-model Mutual Learning for Exemplar-based Medical Image Segmentation","date":"2024-04-18","arxiv_id":"2404.11812","repositories_listed":0,"syntology":null},{"url":null,"slug":"curriculum-point-prompting-for-weakly","title":"Curriculum Point Prompting for Weakly-Supervised Referring Image Segmentation","date":"2024-04-18","arxiv_id":"2404.11998","repositories_listed":0,"syntology":null},{"url":null,"slug":"mixed-prototype-consistency-learning-for-semi","title":"Mixed Prototype Consistency Learning for Semi-supervised Medical Image Segmentation","date":"2024-04-16","arxiv_id":"2404.10717","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-patching-for-high-resolution-image","title":"Adaptive Patching for High-resolution Image Segmentation with Transformers","date":"2024-04-15","arxiv_id":"2404.09707","repositories_listed":0,"syntology":null},{"url":null,"slug":"odformer-semantic-fundus-image-segmentation","title":"ODFormer: Semantic Fundus Image Segmentation Using Transformer for Optic Nerve Head Detection","date":"2024-04-15","arxiv_id":"2405.09552","repositories_listed":0,"syntology":null},{"url":null,"slug":"q2a-querying-implicit-fully-continuous","title":"Q2A: Querying Implicit Fully Continuous Feature Pyramid to Align Features for Medical Image Segmentation","date":"2024-04-15","arxiv_id":"2404.09472","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-revenge-of-bisenet-efficient-multi-task","title":"The revenge of BiSeNet: Efficient Multi-Task Image Segmentation","date":"2024-04-15","arxiv_id":"2404.09570","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-mutual-inclusion-mechanism-for-precise","title":"A Mutual Inclusion Mechanism for Precise Boundary Segmentation in Medical Images","date":"2024-04-12","arxiv_id":"2404.08201","repositories_listed":0,"syntology":null},{"url":null,"slug":"benchmarking-the-cell-image-segmentation","title":"Practical Guidelines for Cell Segmentation Models Under Optical Aberrations in Microscopy","date":"2024-04-12","arxiv_id":"2404.08549","repositories_listed":0,"syntology":null},{"url":null,"slug":"calibration-reconstruction-deep-integrated","title":"Calibration & Reconstruction: Deep Integrated Language for Referring Image Segmentation","date":"2024-04-12","arxiv_id":"2404.08281","repositories_listed":0,"syntology":null},{"url":null,"slug":"glid-pre-training-a-generalist-encoder","title":"GLID: Pre-training a Generalist Encoder-Decoder Vision Model","date":"2024-04-11","arxiv_id":"2404.07603","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-rater-prompting-for-ambiguous-medical","title":"Multi-rater Prompting for Ambiguous Medical Image Segmentation","date":"2024-04-11","arxiv_id":"2404.07580","repositories_listed":0,"syntology":null},{"url":null,"slug":"streamlined-photoacoustic-image-processing","title":"Streamlined Photoacoustic Image Processing with Foundation Models: A Training-Free Solution","date":"2024-04-11","arxiv_id":"2404.07833","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-evidential-enhanced-tri-branch-consistency","title":"An Evidential-enhanced Tri-Branch Consistency Learning Method for Semi-supervised Medical Image Segmentation","date":"2024-04-10","arxiv_id":"2404.07032","repositories_listed":0,"syntology":null},{"url":null,"slug":"o2v-mapping-online-open-vocabulary-mapping","title":"O2V-Mapping: Online Open-Vocabulary Mapping with Neural Implicit Representation","date":"2024-04-10","arxiv_id":"2404.06836","repositories_listed":0,"syntology":null},{"url":null,"slug":"epl-evidential-prototype-learning-for-semi","title":"EPL: Evidential Prototype Learning for Semi-supervised Medical Image Segmentation","date":"2024-04-09","arxiv_id":"2404.06181","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-aware-evidential-fusion-based","title":"Uncertainty-aware Evidential Fusion-based Learning for Semi-supervised Medical Image Segmentation","date":"2024-04-09","arxiv_id":"2404.06177","repositories_listed":0,"syntology":null},{"url":null,"slug":"alignzeg-mitigating-objective-misalignment","title":"AlignZeg: Mitigating Objective Misalignment for Zero-shot Semantic Segmentation","date":"2024-04-08","arxiv_id":"2404.05667","repositories_listed":0,"syntology":null},{"url":null,"slug":"ghost-grounded-human-motion-generation-with","title":"GHOST: Grounded Human Motion Generation with Open Vocabulary Scene-and-Text Contexts","date":"2024-04-08","arxiv_id":"2405.18438","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-based-agarwood-resinous-area","title":"Image-based Agarwood Resinous Area Segmentation using Deep Learning","date":"2024-04-08","arxiv_id":"2404.05129","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-prediction-of-breast-cancer","title":"Automated Prediction of Breast Cancer Response to Neoadjuvant Chemotherapy from DWI Data","date":"2024-04-07","arxiv_id":"2404.05061","repositories_listed":0,"syntology":null},{"url":null,"slug":"platesegfl-a-privacy-preserving-license-plate","title":"PlateSegFL: A Privacy-Preserving License Plate Detection Using Federated Segmentation Learning","date":"2024-04-07","arxiv_id":"2404.05049","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-based-brain-image-segmentation","title":"Deep Learning-Based Brain Image Segmentation for Automated Tumour Detection","date":"2024-04-06","arxiv_id":"2404.05763","repositories_listed":0,"syntology":null},{"url":null,"slug":"mixed-query-transformer-a-unified-image","title":"Mixed-Query Transformer: A Unified Image Segmentation Architecture","date":"2024-04-06","arxiv_id":"2404.04469","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-efficient-and-accurate-ct","title":"Towards Efficient and Accurate CT Segmentation via Edge-Preserving Probabilistic Downsampling","date":"2024-04-05","arxiv_id":"2404.03991","repositories_listed":0,"syntology":null},{"url":null,"slug":"opennerf-open-set-3d-neural-scene","title":"OpenNeRF: Open Set 3D Neural Scene Segmentation with Pixel-Wise Features and Rendered Novel Views","date":"2024-04-04","arxiv_id":"2404.03650","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigation-of-energy-efficient-ai-model","title":"Investigation of Energy-efficient AI Model Architectures and Compression Techniques for \"Green\" Fetal Brain Segmentation","date":"2024-04-03","arxiv_id":"2405.15778","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-feature-fusion-neural-network-for","title":"Adaptive Feature Fusion Neural Network for Glaucoma Segmentation on Unseen Fundus Images","date":"2024-04-02","arxiv_id":"2404.02084","repositories_listed":0,"syntology":null},{"url":null,"slug":"diffusion-based-zero-shot-medical-image-to","title":"Diffusion based Zero-shot Medical Image-to-Image Translation for Cross Modality Segmentation","date":"2024-04-01","arxiv_id":"2404.01102","repositories_listed":0,"syntology":null},{"url":null,"slug":"medical-visual-prompting-mvp-a-unified","title":"Medical Visual Prompting (MVP): A Unified Framework for Versatile and High-Quality Medical Image Segmentation","date":"2024-04-01","arxiv_id":"2404.01127","repositories_listed":0,"syntology":null},{"url":null,"slug":"ovfoodseg-elevating-open-vocabulary-food","title":"OVFoodSeg: Elevating Open-Vocabulary Food Image Segmentation via Image-Informed Textual Representation","date":"2024-04-01","arxiv_id":"2404.01409","repositories_listed":0,"syntology":null},{"url":null,"slug":"teeth-seg-an-efficient-instance-segmentation","title":"Teeth-SEG: An Efficient Instance Segmentation Framework for Orthodontic Treatment based on Anthropic Prior Knowledge","date":"2024-04-01","arxiv_id":"2404.01013","repositories_listed":0,"syntology":null},{"url":null,"slug":"mugennet-a-novel-combined-convolution-neural","title":"MugenNet: A Novel Combined Convolution Neural Network and Transformer Network with its Application for Colonic Polyp Image Segmentation","date":"2024-03-31","arxiv_id":"2404.00726","repositories_listed":0,"syntology":null},{"url":null,"slug":"freeseg-diff-training-free-open-vocabulary","title":"FreeSeg-Diff: Training-Free Open-Vocabulary Segmentation with Diffusion Models","date":"2024-03-29","arxiv_id":"2403.20105","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-3d-instance-mapping-and","title":"Efficient 3D Instance Mapping and Localization with Neural Fields","date":"2024-03-28","arxiv_id":"2403.19797","repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-information-loss-in-medical-image","title":"Rethinking Information Loss in Medical Image Segmentation with Various-sized Targets","date":"2024-03-28","arxiv_id":"2403.19177","repositories_listed":0,"syntology":null},{"url":null,"slug":"segmentation-re-thinking-uncertainty","title":"Segmentation Re-thinking Uncertainty Estimation Metrics for Semantic Segmentation","date":"2024-03-28","arxiv_id":"2403.19826","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-quantum-fuzzy-based-approach-for-real-time","title":"A Quantum Fuzzy-based Approach for Real-Time Detection of Solar Coronal Holes","date":"2024-03-27","arxiv_id":"2403.18347","repositories_listed":0,"syntology":null},{"url":null,"slug":"aic-unet-anatomy-informed-cascaded-unet-for","title":"Teaching AI the Anatomy Behind the Scan: Addressing Anatomical Flaws in Medical Image Segmentation with Learnable Prior","date":"2024-03-27","arxiv_id":"2403.18878","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-segmentation-and-classification","title":"Deep Learning Segmentation and Classification of Red Blood Cells Using a Large Multi-Scanner Dataset","date":"2024-03-27","arxiv_id":"2403.18468","repositories_listed":0,"syntology":null},{"url":null,"slug":"ct-synthesis-with-conditional-diffusion","title":"CT Synthesis with Conditional Diffusion Models for Abdominal Lymph Node Segmentation","date":"2024-03-26","arxiv_id":"2403.17770","repositories_listed":0,"syntology":null},{"url":null,"slug":"integrating-mamba-sequence-model-and","title":"Integrating Mamba Sequence Model and Hierarchical Upsampling Network for Accurate Semantic Segmentation of Multiple Sclerosis Legion","date":"2024-03-26","arxiv_id":"2403.17432","repositories_listed":0,"syntology":null},{"url":null,"slug":"random-coupled-neural-network","title":"Random-coupled Neural Network","date":"2024-03-26","arxiv_id":"2403.17512","repositories_listed":0,"syntology":null},{"url":null,"slug":"rotate-to-scan-unet-like-mamba-with-triplet","title":"Rotate to Scan: UNet-like Mamba with Triplet SSM Module for Medical Image Segmentation","date":"2024-03-26","arxiv_id":"2403.17701","repositories_listed":0,"syntology":null},{"url":null,"slug":"brain-stroke-segmentation-using-deep-learning","title":"Deep models for stroke segmentation: do complex architectures always perform better?","date":"2024-03-25","arxiv_id":"2403.17177","repositories_listed":0,"syntology":null},{"url":null,"slug":"chebmixer-efficient-graph-representation","title":"ChebMixer: Efficient Graph Representation Learning with MLP Mixer","date":"2024-03-25","arxiv_id":"2403.16358","repositories_listed":0,"syntology":null},{"url":null,"slug":"edue-expert-disagreement-guided-one-pass","title":"EDUE: Expert Disagreement-Guided One-Pass Uncertainty Estimation for Medical Image Segmentation","date":"2024-03-25","arxiv_id":"2403.16594","repositories_listed":0,"syntology":null},{"url":null,"slug":"segicl-a-universal-in-context-learning","title":"SegICL: A Multimodal In-context Learning Framework for Enhanced Segmentation in Medical Imaging","date":"2024-03-25","arxiv_id":"2403.16578","repositories_listed":0,"syntology":null},{"url":null,"slug":"strategies-to-improve-real-world","title":"Strategies to Improve Real-World Applicability of Laparoscopic Anatomy Segmentation Models","date":"2024-03-25","arxiv_id":"2403.17192","repositories_listed":0,"syntology":null},{"url":null,"slug":"sm2c-boost-the-semi-supervised-segmentation","title":"SM2C: Boost the Semi-supervised Segmentation for Medical Image by using Meta Pseudo Labels and Mixed Images","date":"2024-03-24","arxiv_id":"2403.16009","repositories_listed":0,"syntology":null},{"url":null,"slug":"modular-deep-active-learning-framework-for","title":"Modular Deep Active Learning Framework for Image Annotation: A Technical Report for the Ophthalmo-AI Project","date":"2024-03-22","arxiv_id":"2403.15143","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysing-diffusion-segmentation-for-medical","title":"Analysing Diffusion Segmentation for Medical Images","date":"2024-03-21","arxiv_id":"2403.14440","repositories_listed":0,"syntology":null},{"url":null,"slug":"masksam-towards-auto-prompt-sam-with-mask","title":"MaskSAM: Towards Auto-prompt SAM with Mask Classification for Medical Image Segmentation","date":"2024-03-21","arxiv_id":"2403.14103","repositories_listed":0,"syntology":null},{"url":null,"slug":"safeguarding-medical-image-segmentation","title":"Safeguarding Medical Image Segmentation Datasets against Unauthorized Training via Contour- and Texture-Aware Perturbations","date":"2024-03-21","arxiv_id":"2403.14250","repositories_listed":0,"syntology":null},{"url":null,"slug":"promamba-prompt-mamba-for-polyp-segmentation","title":"ProMamba: Prompt-Mamba for polyp segmentation","date":"2024-03-20","arxiv_id":"2403.13660","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-image-segmentation-model-based-on","title":"Robust image segmentation model based on binary level set","date":"2024-03-20","arxiv_id":"2403.13392","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-driven-active-learning-for-image","title":"Uncertainty Driven Active Learning for Image Segmentation in Underwater Inspection","date":"2024-03-20","arxiv_id":"2403.14002","repositories_listed":0,"syntology":null},{"url":null,"slug":"building-brain-tumor-segmentation-networks","title":"Building Brain Tumor Segmentation Networks with User-Assisted Filter Estimation and Selection","date":"2024-03-19","arxiv_id":"2403.12748","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-semi-supervised-learning-for","title":"Federated Semi-supervised Learning for Medical Image Segmentation with intra-client and inter-client Consistency","date":"2024-03-19","arxiv_id":"2403.12695","repositories_listed":0,"syntology":null},{"url":null,"slug":"qubiq-uncertainty-quantification-for","title":"QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge","date":"2024-03-19","arxiv_id":"2405.18435","repositories_listed":0,"syntology":null}],"record_sha256":"11c3409de9b2d1680fd7ee315fc369ab439844c0c3488f79298ba826db1b7f30","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}