{"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/segmentation/papers/67","list_of":"/task/segmentation","task":"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":67,"pages_in_order":131,"rows_per_page":100,"rows":[6601,6700],"of":13072,"counts":{"archive_papers_tagged":13072,"with_a_code_link":5255,"where_syntology_ran_a_sample":976,"not_listed_spam_title":0,"listed":13072,"listed_where_code_ran":976,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":838,"every_run_a_failure_of_syntologys_instrument":138,"listed_with_a_run_with_no_instrument_failure":838,"listed_every_run_a_failure_of_syntologys_instrument":138,"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/segmentation","prev":"/task/segmentation/papers/66","next":"/task/segmentation/papers/68","papers":[{"url":null,"slug":"tendency-driven-mutual-exclusivity-for-weakly","title":"Tendency-driven Mutual Exclusivity for Weakly Supervised Incremental Semantic Segmentation","date":"2024-04-18","arxiv_id":"2404.11981","repositories_listed":0,"syntology":null},{"url":null,"slug":"akgnet-attribute-knowledge-guided","title":"AKGNet: Attribute Knowledge-Guided Unsupervised Lung-Infected Area Segmentation","date":"2024-04-17","arxiv_id":"2404.11008","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-decomposition-dynamic-segmentation","title":"Meta-Decomposition: Dynamic Segmentation Approach Selection in IoT-based Activity Recognition","date":"2024-04-17","arxiv_id":"2404.11742","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-target-and-multi-stage-liver-lesion","title":"Multi-target and multi-stage liver lesion segmentation and detection in multi-phase computed tomography scans","date":"2024-04-17","arxiv_id":"2404.11152","repositories_listed":0,"syntology":null},{"url":"/paper/wps-dataset-a-benchmark-for-wood-plate","slug":"wps-dataset-a-benchmark-for-wood-plate","title":"WPS-Dataset: A benchmark for wood plate segmentation in bark removal processing","date":"2024-04-17","arxiv_id":"2404.11051","repositories_listed":0,"syntology":null},{"url":null,"slug":"adapting-sam-for-surgical-instrument-tracking","title":"Adapting SAM for Surgical Instrument Tracking and Segmentation in Endoscopic Submucosal Dissection Videos","date":"2024-04-16","arxiv_id":"2404.10640","repositories_listed":0,"syntology":null},{"url":null,"slug":"contextrast-contextual-contrastive-learning","title":"Contextrast: Contextual Contrastive Learning for Semantic Segmentation","date":"2024-04-16","arxiv_id":"2404.10633","repositories_listed":0,"syntology":null},{"url":null,"slug":"label-merge-and-split-a-graph-colouring","title":"Label merge-and-split: A graph-colouring approach for memory-efficient brain parcellation","date":"2024-04-16","arxiv_id":"2404.10572","repositories_listed":0,"syntology":null},{"url":null,"slug":"neuromorphic-vision-based-motion-segmentation","title":"Neuromorphic Vision-based Motion Segmentation with Graph Transformer Neural Network","date":"2024-04-16","arxiv_id":"2404.10940","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":"deep-learning-based-segmentation-of-tumors-in","title":"Deep Learning-Based Segmentation of Tumors in PET/CT Volumes: Benchmark of Different Architectures and Training Strategies","date":"2024-04-15","arxiv_id":"2404.09761","repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-object-based-style-transfer-with","title":"Improved Object-Based Style Transfer with Single Deep Network","date":"2024-04-15","arxiv_id":"2404.09461","repositories_listed":0,"syntology":null},{"url":null,"slug":"in-context-translation-towards-unifying-image","title":"In-Context Translation: Towards Unifying Image Recognition, Processing, and Generation","date":"2024-04-15","arxiv_id":"2404.09633","repositories_listed":0,"syntology":null},{"url":null,"slug":"knn-clip-retrieval-enables-training-free","title":"kNN-CLIP: Retrieval Enables Training-Free Segmentation on Continually Expanding Large Vocabularies","date":"2024-04-15","arxiv_id":"2404.09447","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":"approximate-cluster-based-sparse-document","title":"Approximate Cluster-Based Sparse Document Retrieval with Segmented Maximum Term Weights","date":"2024-04-13","arxiv_id":"2404.08896","repositories_listed":0,"syntology":null},{"url":null,"slug":"labeled-morphological-segmentation-with-semi-1","title":"Labeled Morphological Segmentation with Semi-Markov Models","date":"2024-04-13","arxiv_id":"2404.08997","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":"adapting-the-segment-anything-model-during","title":"Adapting the Segment Anything Model During Usage in Novel Situations","date":"2024-04-12","arxiv_id":"2404.08421","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":"coconut-modernizing-coco-segmentation","title":"COCONut: Modernizing COCO Segmentation","date":"2024-04-12","arxiv_id":"2404.08639","repositories_listed":0,"syntology":null},{"url":null,"slug":"diffusion-probabilistic-multi-cue-level-set","title":"Diffusion Probabilistic Multi-cue Level Set for Reducing Edge Uncertainty in Pancreas Segmentation","date":"2024-04-11","arxiv_id":"2404.07620","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-object-based-and-segmentation","title":"Exploiting Object-based and Segmentation-based Semantic Features for Deep Learning-based Indoor Scene Classification","date":"2024-04-11","arxiv_id":"2404.07739","repositories_listed":0,"syntology":null},{"url":null,"slug":"gaga-group-any-gaussians-via-3d-aware-memory","title":"Gaga: Group Any Gaussians via 3D-aware Memory Bank","date":"2024-04-11","arxiv_id":"2404.07977","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":"weakly-supervised-learning-via-multi-lateral","title":"Weakly-Supervised Learning via Multi-Lateral Decoder Branching for Tool Segmentation in Robot-Assisted Cardiovascular Catheterization","date":"2024-04-11","arxiv_id":"2404.07594","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":"o-talc-steps-towards-combating","title":"O-TALC: Steps Towards Combating Oversegmentation within Online Action Segmentation","date":"2024-04-10","arxiv_id":"2404.06894","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":"daf-bevseg-distortion-aware-fisheye-camera","title":"DaF-BEVSeg: Distortion-aware Fisheye Camera based Bird's Eye View Segmentation with Occlusion Reasoning","date":"2024-04-09","arxiv_id":"2404.06352","repositories_listed":0,"syntology":null},{"url":null,"slug":"latup-net-a-lightweight-3d-attention-u-net","title":"LATUP-Net: A Lightweight 3D Attention U-Net with Parallel Convolutions for Brain Tumor Segmentation","date":"2024-04-09","arxiv_id":"2404.05911","repositories_listed":0,"syntology":null},{"url":null,"slug":"prompt-driven-universal-model-for-view","title":"Prompt-driven Universal Model for View-Agnostic Echocardiography Analysis","date":"2024-04-09","arxiv_id":"2404.05916","repositories_listed":0,"syntology":null},{"url":null,"slug":"questmaps-queryable-semantic-topological-maps","title":"QueSTMaps: Queryable Semantic Topological Maps for 3D Scene Understanding","date":"2024-04-09","arxiv_id":"2404.06442","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatial-temporal-multi-level-association-for","title":"Spatial-Temporal Multi-level Association for Video Object Segmentation","date":"2024-04-09","arxiv_id":"2404.06265","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":"comparative-analysis-of-image-enhancement","title":"Comparative Analysis of Image Enhancement Techniques for Brain Tumor Segmentation: Contrast, Histogram, and Hybrid Approaches","date":"2024-04-08","arxiv_id":"2404.05341","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":"d2sl-decouple-defogging-and-semantic-learning","title":"D2SL: Decouple Defogging and Semantic Learning for Foggy Domain-Adaptive Segmentation","date":"2024-04-07","arxiv_id":"2404.04807","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":"panoptic-perception-a-novel-task-and-fine","title":"Panoptic Perception: A Novel Task and Fine-grained Dataset for Universal Remote Sensing Image Interpretation","date":"2024-04-06","arxiv_id":"2404.04608","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-segmentation-of-small-volumes","title":"Deep-learning Segmentation of Small Volumes in CT images for Radiotherapy Treatment Planning","date":"2024-04-05","arxiv_id":"2404.04202","repositories_listed":0,"syntology":null},{"url":null,"slug":"influence-based-explainability-of-brain","title":"Influence based explainability of brain tumors segmentation in multimodal Magnetic Resonance Imaging","date":"2024-04-05","arxiv_id":"2405.12222","repositories_listed":0,"syntology":null},{"url":null,"slug":"marsseg-mars-surface-semantic-segmentation","title":"MarsSeg: Mars Surface Semantic Segmentation with Multi-level Extractor and Connector","date":"2024-04-05","arxiv_id":"2404.04155","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":"background-noise-reduction-of-attention-map","title":"Background Noise Reduction of Attention Map for Weakly Supervised Semantic Segmentation","date":"2024-04-04","arxiv_id":"2404.03394","repositories_listed":0,"syntology":null},{"url":null,"slug":"iseg-interactive-3d-segmentation-via","title":"iSeg: Interactive 3D Segmentation via Interactive Attention","date":"2024-04-04","arxiv_id":"2404.03219","repositories_listed":0,"syntology":null},{"url":null,"slug":"language-guided-instance-aware-domain","title":"Language-Guided Instance-Aware Domain-Adaptive Panoptic Segmentation","date":"2024-04-04","arxiv_id":"2404.03799","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":"ow-viscap-open-world-video-instance","title":"OW-VISCapTor: Abstractors for Open-World Video Instance Segmentation and Captioning","date":"2024-04-04","arxiv_id":"2404.03657","repositories_listed":0,"syntology":null},{"url":null,"slug":"segmentation-guided-knee-radiograph","title":"Segmentation-Guided Knee Radiograph Generation using Conditional Diffusion Models","date":"2024-04-04","arxiv_id":"2404.03541","repositories_listed":0,"syntology":null},{"url":null,"slug":"test-time-training-for-industrial-anomaly","title":"Test Time Training for Industrial Anomaly Segmentation","date":"2024-04-04","arxiv_id":"2404.03743","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-affinity-based-generalization-for","title":"Adaptive Affinity-Based Generalization For MRI Imaging Segmentation Across Resource-Limited Settings","date":"2024-04-03","arxiv_id":"2404.02738","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":"automating-vessel-segmentation-in-the-heart","title":"Automating Vessel Segmentation in the Heart and Brain: A Trend to Develop Multi-Modality and Label-Efficient Deep Learning Techniques","date":"2024-04-02","arxiv_id":"2404.01671","repositories_listed":0,"syntology":null},{"url":null,"slug":"guidelines-for-cerebrovascular-segmentation","title":"Guidelines for Cerebrovascular Segmentation: Managing Imperfect Annotations in the context of Semi-Supervised Learning","date":"2024-04-02","arxiv_id":"2404.01765","repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-annotator-simulation-realistic","title":"Rethinking Annotator Simulation: Realistic Evaluation of Whole-Body PET Lesion Interactive Segmentation Methods","date":"2024-04-02","arxiv_id":"2404.01816","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":"towards-label-efficient-human-matting-a","title":"Towards Label-Efficient Human Matting: A Simple Baseline for Weakly Semi-Supervised Trimap-Free Human Matting","date":"2024-04-01","arxiv_id":"2404.00921","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":"training-free-semantic-segmentation-via-llm","title":"Training-Free Semantic Segmentation via LLM-Supervision","date":"2024-03-31","arxiv_id":"2404.00701","repositories_listed":0,"syntology":null},{"url":null,"slug":"ynetr-dual-encoder-architecture-on-plain-scan","title":"YNetr: Dual-Encoder architecture on Plain Scan Liver Tumors (PSLT)","date":"2024-03-30","arxiv_id":"2404.00327","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-stage-semi-supervised-learning-for","title":"A multi-stage semi-supervised learning for ankle fracture classification on CT images","date":"2024-03-29","arxiv_id":"2403.19983","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":"segmentation-classification-and","title":"Segmentation, Classification and Interpretation of Breast Cancer Medical Images using Human-in-the-Loop Machine Learning","date":"2024-03-29","arxiv_id":"2403.20112","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":"enet-21-an-optimized-light-cnn-structure-for","title":"ENet-21: An Optimized light CNN Structure for Lane Detection","date":"2024-03-28","arxiv_id":"2403.19782","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-multiple-representations-with","title":"Learning Multiple Representations with Inconsistency-Guided Detail Regularization for Mask-Guided Matting","date":"2024-03-28","arxiv_id":"2403.19213","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":"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":"i2ckd-intra-and-inter-class-knowledge","title":"I2CKD : Intra- and Inter-Class Knowledge Distillation for Semantic Segmentation","date":"2024-03-27","arxiv_id":"2403.18490","repositories_listed":0,"syntology":null},{"url":null,"slug":"transformers-based-architectures-for-stroke","title":"Transformers-based architectures for stroke segmentation: A review","date":"2024-03-27","arxiv_id":"2403.18637","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":"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":"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":"hpl-ess-hybrid-pseudo-labeling-for","title":"HPL-ESS: Hybrid Pseudo-Labeling for Unsupervised Event-based Semantic Segmentation","date":"2024-03-25","arxiv_id":"2403.16788","repositories_listed":0,"syntology":null},{"url":null,"slug":"satsynth-augmenting-image-mask-pairs-through","title":"SatSynth: Augmenting Image-Mask Pairs through Diffusion Models for Aerial Semantic Segmentation","date":"2024-03-25","arxiv_id":"2403.16605","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":"an-information-theoretic-treatment-of-animal","title":"An Information Theory Treatment of Animal Movement Tracks","date":"2024-03-24","arxiv_id":"2403.16290","repositories_listed":0,"syntology":null},{"url":null,"slug":"entity-nerf-detecting-and-removing-moving","title":"Entity-NeRF: Detecting and Removing Moving Entities in Urban Scenes","date":"2024-03-24","arxiv_id":"2403.16141","repositories_listed":0,"syntology":null},{"url":null,"slug":"hemoset-the-first-blood-segmentation-dataset","title":"HemoSet: The First Blood Segmentation Dataset for Automation of Hemostasis Management","date":"2024-03-24","arxiv_id":"2403.16286","repositories_listed":0,"syntology":null},{"url":null,"slug":"pshop-a-lightweight-feed-forward-method-for","title":"PSHop: A Lightweight Feed-Forward Method for 3D Prostate Gland Segmentation","date":"2024-03-24","arxiv_id":"2403.15971","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":"inpainting-driven-mask-optimization-for","title":"Inpainting-Driven Mask Optimization for Object Removal","date":"2024-03-23","arxiv_id":"2403.15849","repositories_listed":0,"syntology":null},{"url":null,"slug":"ifsenet-harnessing-sparse-iterations-for","title":"IFSENet : Harnessing Sparse Iterations for Interactive Few-shot Segmentation Excellence","date":"2024-03-22","arxiv_id":"2403.15089","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":"semantic-gaussians-open-vocabulary-scene","title":"Semantic Gaussians: Open-Vocabulary Scene Understanding with 3D Gaussian Splatting","date":"2024-03-22","arxiv_id":"2403.15624","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-automatic-abdominal-mri-organ","title":"Towards Automatic Abdominal MRI Organ Segmentation: Leveraging Synthesized Data Generated From CT Labels","date":"2024-03-22","arxiv_id":"2403.15609","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":"annotation-efficient-polyp-segmentation-via","title":"Annotation-Efficient Polyp Segmentation via Active Learning","date":"2024-03-21","arxiv_id":"2403.14350","repositories_listed":0,"syntology":null},{"url":null,"slug":"cathflow-self-supervised-segmentation-of","title":"CathFlow: Self-Supervised Segmentation of Catheters in Interventional Ultrasound Using Optical Flow and Transformers","date":"2024-03-21","arxiv_id":"2403.14465","repositories_listed":0,"syntology":null}],"record_sha256":"aa566ab7d2b2ff9bdf839d0d121053c5044370e015d2baee5ee5251e9cd96f69","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}