{"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/medical-image-segmentation/papers/5","list_of":"/task/medical-image-segmentation","task":"Medical 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":5,"pages_in_order":21,"rows_per_page":100,"rows":[401,500],"of":2089,"counts":{"archive_papers_tagged":2089,"with_a_code_link":1080,"where_syntology_ran_a_sample":190,"not_listed_spam_title":0,"listed":2089,"listed_where_code_ran":190,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":164,"every_run_a_failure_of_syntologys_instrument":26,"listed_with_a_run_with_no_instrument_failure":164,"listed_every_run_a_failure_of_syntologys_instrument":26,"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/medical-image-segmentation","prev":"/task/medical-image-segmentation/papers/4","next":"/task/medical-image-segmentation/papers/6","papers":[{"url":"/paper/lora-pt-low-rank-adapting-unetr-for","slug":"lora-pt-low-rank-adapting-unetr-for","title":"LoRA-PT: Low-Rank Adapting UNETR for Hippocampus Segmentation Using Principal Tensor Singular Values and Vectors","date":"2024-07-16","arxiv_id":"2407.11292","repositories_listed":1,"syntology":null},{"url":"/paper/the-devil-is-in-the-statistics-mitigating-and","slug":"the-devil-is-in-the-statistics-mitigating-and","title":"The Devil is in the Statistics: Mitigating and Exploiting Statistics Difference for Generalizable Semi-supervised Medical Image Segmentation","date":"2024-07-16","arxiv_id":"2407.11356","repositories_listed":1,"syntology":null},{"url":"/paper/diffrect-latent-diffusion-label-rectification","slug":"diffrect-latent-diffusion-label-rectification","title":"DiffRect: Latent Diffusion Label Rectification for Semi-supervised Medical Image Segmentation","date":"2024-07-13","arxiv_id":"2407.09918","repositories_listed":1,"syntology":null},{"url":"/paper/segmenting-medical-images-with-limited-data","slug":"segmenting-medical-images-with-limited-data","title":"Segmenting Medical Images with Limited Data","date":"2024-07-12","arxiv_id":"2407.09189","repositories_listed":1,"syntology":null},{"url":"/paper/fairdomain-achieving-fairness-in-cross-domain","slug":"fairdomain-achieving-fairness-in-cross-domain","title":"FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classification","date":"2024-07-11","arxiv_id":"2407.08813","repositories_listed":1,"syntology":null},{"url":"/paper/swin-smt-global-sequential-modeling-in-3d","slug":"swin-smt-global-sequential-modeling-in-3d","title":"Swin SMT: Global Sequential Modeling in 3D Medical Image Segmentation","date":"2024-07-10","arxiv_id":"2407.07514","repositories_listed":1,"syntology":null},{"url":"/paper/weakly-supervised-medical-image-segmentation-2","slug":"weakly-supervised-medical-image-segmentation-2","title":"Weakly-supervised Medical Image Segmentation with Gaze Annotations","date":"2024-07-10","arxiv_id":"2407.07406","repositories_listed":1,"syntology":null},{"url":"/paper/anatomask-enhancing-medical-image","slug":"anatomask-enhancing-medical-image","title":"AnatoMask: Enhancing Medical Image Segmentation with Reconstruction-guided Self-masking","date":"2024-07-09","arxiv_id":"2407.06468","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":2,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/anatomask-enhancing-medical-image#ran","syntology_url":"https://syntology.ai/paper/2407.06468","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.06468"}},"official":{"repos":["ricklisz/anatomask"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/protosam-one-shot-medical-image-segmentation","slug":"protosam-one-shot-medical-image-segmentation","title":"ProtoSAM: One-Shot Medical Image Segmentation With Foundational Models","date":"2024-07-09","arxiv_id":"2407.07042","repositories_listed":1,"syntology":null},{"url":"/paper/interpretability-of-uncertainty-exploring","slug":"interpretability-of-uncertainty-exploring","title":"Interpretability of Uncertainty: Exploring Cortical Lesion Segmentation in Multiple Sclerosis","date":"2024-07-08","arxiv_id":"2407.05761","repositories_listed":1,"syntology":null},{"url":"/paper/cross-prompting-consistency-with-segment","slug":"cross-prompting-consistency-with-segment","title":"Cross Prompting Consistency with Segment Anything Model for Semi-supervised Medical Image Segmentation","date":"2024-07-07","arxiv_id":"2407.05416","repositories_listed":1,"syntology":null},{"url":"/paper/self-paced-sample-selection-for-barely","slug":"self-paced-sample-selection-for-barely","title":"Self-Paced Sample Selection for Barely-Supervised Medical Image Segmentation","date":"2024-07-07","arxiv_id":"2407.05248","repositories_listed":1,"syntology":null},{"url":"/paper/an-uncertainty-guided-tiered-self-training","slug":"an-uncertainty-guided-tiered-self-training","title":"An Uncertainty-guided Tiered Self-training Framework for Active Source-free Domain Adaptation in Prostate Segmentation","date":"2024-07-03","arxiv_id":"2407.02893","repositories_listed":1,"syntology":null},{"url":"/paper/hidiff-hybrid-diffusion-framework-for-medical","slug":"hidiff-hybrid-diffusion-framework-for-medical","title":"HiDiff: Hybrid Diffusion Framework for Medical Image Segmentation","date":"2024-07-03","arxiv_id":"2407.03548","repositories_listed":1,"syntology":null},{"url":"/paper/fedia-federated-medical-image-segmentation","slug":"fedia-federated-medical-image-segmentation","title":"FedIA: Federated Medical Image Segmentation with Heterogeneous Annotation Completeness","date":"2024-07-02","arxiv_id":"2407.02280","repositories_listed":1,"syntology":null},{"url":"/paper/domain-influence-in-mri-medical-image","slug":"domain-influence-in-mri-medical-image","title":"Domain Influence in MRI Medical Image Segmentation: spatial versus k-space inputs","date":"2024-07-01","arxiv_id":"2407.01367","repositories_listed":1,"syntology":null},{"url":"/paper/xlstm-unet-can-be-an-effective-2d-3d-medical","slug":"xlstm-unet-can-be-an-effective-2d-3d-medical","title":"xLSTM-UNet can be an Effective 2D & 3D Medical Image Segmentation Backbone with Vision-LSTM (ViL) better than its Mamba Counterpart","date":"2024-07-01","arxiv_id":"2407.01530","repositories_listed":1,"syntology":null},{"url":"/paper/astmatch-adversarial-self-training","slug":"astmatch-adversarial-self-training","title":"AstMatch: Adversarial Self-training Consistency Framework for Semi-Supervised Medical Image Segmentation","date":"2024-06-28","arxiv_id":"2406.19649","repositories_listed":1,"syntology":null},{"url":"/paper/simtxtseg-weakly-supervised-medical-image","slug":"simtxtseg-weakly-supervised-medical-image","title":"SimTxtSeg: Weakly-Supervised Medical Image Segmentation with Simple Text Cues","date":"2024-06-27","arxiv_id":"2406.19364","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-medical-image-segmentation-with-high","slug":"few-shot-medical-image-segmentation-with-high","title":"Few-Shot Medical Image Segmentation with High-Fidelity Prototypes","date":"2024-06-26","arxiv_id":"2406.18074","repositories_listed":1,"syntology":null},{"url":"/paper/stable-diffusion-segmentation-for-biomedical","slug":"stable-diffusion-segmentation-for-biomedical","title":"Stable Diffusion Segmentation for Biomedical Images with Single-step Reverse Process","date":"2024-06-26","arxiv_id":"2406.18361","repositories_listed":1,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":4,"n_instrument":5,"n_unverified":0,"n_honours":0,"n_violates":3,"n_no_contract":1,"n_pointer_only":9,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 3 violated, 1 with no contract checked; 5 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/stable-diffusion-segmentation-for-biomedical#ran","syntology_url":"https://syntology.ai/paper/2406.18361","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.18361"}},"official":{"repos":["lin-tianyu/stable-diffusion-seg"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-synchronous-memorizability-and","slug":"towards-synchronous-memorizability-and","title":"Towards Synchronous Memorizability and Generalizability with Site-Modulated Diffusion Replay for Cross-Site Continual Segmentation","date":"2024-06-26","arxiv_id":"2406.18037","repositories_listed":1,"syntology":null},{"url":"/paper/domain-adaptation-of-echocardiography","slug":"domain-adaptation-of-echocardiography","title":"Domain Adaptation of Echocardiography Segmentation Via Reinforcement Learning","date":"2024-06-25","arxiv_id":"2406.17902","repositories_listed":1,"syntology":null},{"url":"/paper/medical-image-segmentation-using-directional","slug":"medical-image-segmentation-using-directional","title":"Medical Image Segmentation Using Directional Window Attention","date":"2024-06-25","arxiv_id":"2406.17471","repositories_listed":1,"syntology":null},{"url":"/paper/test-time-generative-augmentation-for-medical","slug":"test-time-generative-augmentation-for-medical","title":"Test-Time Generative Augmentation for Medical Image Segmentation","date":"2024-06-25","arxiv_id":"2406.17608","repositories_listed":1,"syntology":null},{"url":"/paper/demystifying-the-effect-of-receptive-field","slug":"demystifying-the-effect-of-receptive-field","title":"Demystifying the Effect of Receptive Field Size in U-Net Models for Medical Image Segmentation","date":"2024-06-24","arxiv_id":"2406.16701","repositories_listed":1,"syntology":null},{"url":"/paper/selfreg-unet-self-regularized-unet-for","slug":"selfreg-unet-self-regularized-unet-for","title":"SelfReg-UNet: Self-Regularized UNet for Medical Image Segmentation","date":"2024-06-21","arxiv_id":"2406.14896","repositories_listed":1,"syntology":null},{"url":"/paper/cridiff-criss-cross-injection-diffusion","slug":"cridiff-criss-cross-injection-diffusion","title":"CriDiff: Criss-cross Injection Diffusion Framework via Generative Pre-train for Prostate Segmentation","date":"2024-06-20","arxiv_id":"2406.14186","repositories_listed":1,"syntology":null},{"url":"/paper/perspective-unet-enhancing-segmentation-with","slug":"perspective-unet-enhancing-segmentation-with","title":"Perspective+ Unet: Enhancing Segmentation with Bi-Path Fusion and Efficient Non-Local Attention for Superior Receptive Fields","date":"2024-06-20","arxiv_id":"2406.14052","repositories_listed":1,"syntology":null},{"url":"/paper/alps-an-auto-labeling-and-pre-training-scheme","slug":"alps-an-auto-labeling-and-pre-training-scheme","title":"ALPS: An Auto-Labeling and Pre-training Scheme for Remote Sensing Segmentation With Segment Anything Model","date":"2024-06-16","arxiv_id":"2406.10855","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-incomplete-multi-modal-brain-tumor","slug":"enhancing-incomplete-multi-modal-brain-tumor","title":"Enhancing Incomplete Multi-modal Brain Tumor Segmentation with Intra-modal Asymmetry and Inter-modal Dependency","date":"2024-06-14","arxiv_id":"2406.10175","repositories_listed":1,"syntology":null},{"url":"/paper/spatial-frequency-dual-progressive-attention","slug":"spatial-frequency-dual-progressive-attention","title":"Spatial-Frequency Dual Progressive Attention Network For Medical Image Segmentation","date":"2024-06-12","arxiv_id":"2406.07952","repositories_listed":1,"syntology":null},{"url":"/paper/convolution-and-attention-free-mamba-based","slug":"convolution-and-attention-free-mamba-based","title":"CAMS: Convolution and Attention-Free Mamba-based Cardiac Image Segmentation","date":"2024-06-09","arxiv_id":"2406.05786","repositories_listed":1,"syntology":null},{"url":"/paper/gctx-unet-efficient-network-for-medical-image","slug":"gctx-unet-efficient-network-for-medical-image","title":"GCtx-UNet: Efficient Network for Medical Image Segmentation","date":"2024-06-09","arxiv_id":"2406.05891","repositories_listed":1,"syntology":null},{"url":"/paper/3d-mri-synthesis-with-slice-based-latent","slug":"3d-mri-synthesis-with-slice-based-latent","title":"3D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving Tumor Segmentation Tasks in Data-Scarce Regimes","date":"2024-06-08","arxiv_id":"2406.05421","repositories_listed":1,"syntology":null},{"url":"/paper/vista3d-versatile-imaging-segmentation-and","slug":"vista3d-versatile-imaging-segmentation-and","title":"VISTA3D: Versatile Imaging SegmenTation and Annotation model for 3D Computed Tomography","date":"2024-06-07","arxiv_id":"2406.05285","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/vista3d-versatile-imaging-segmentation-and#ran","syntology_url":"https://syntology.ai/paper/2406.05285","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.05285"}},"official":{"repos":["Project-MONAI/VISTA"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-task-multi-scale-contrastive-knowledge","slug":"multi-task-multi-scale-contrastive-knowledge","title":"Multi-Task Multi-Scale Contrastive Knowledge Distillation for Efficient Medical Image Segmentation","date":"2024-06-05","arxiv_id":"2406.03173","repositories_listed":1,"syntology":null},{"url":"/paper/simsam-zero-shot-medical-image-segmentation","slug":"simsam-zero-shot-medical-image-segmentation","title":"SimSAM: Zero-shot Medical Image Segmentation via Simulated Interaction","date":"2024-06-02","arxiv_id":"2406.00663","repositories_listed":1,"syntology":null},{"url":"/paper/cbar-unet-a-novel-methodology-for","slug":"cbar-unet-a-novel-methodology-for","title":"CBAR‑UNet: A novel methodology for segmentation of cardiac magnetic resonance images using block attention‑based deep residual neural network","date":"2024-05-29","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/duedl-dual-branch-evidential-deep-learning","slug":"duedl-dual-branch-evidential-deep-learning","title":"DuEDL: Dual-Branch Evidential Deep Learning for Scribble-Supervised Medical Image Segmentation","date":"2024-05-23","arxiv_id":"2405.14444","repositories_listed":1,"syntology":null},{"url":"/paper/hemseg-200-a-voxel-annotated-dataset-for","slug":"hemseg-200-a-voxel-annotated-dataset-for","title":"HemSeg-200: A Voxel-Annotated Dataset for Intracerebral Hemorrhages Segmentation in Brain CT Scans","date":"2024-05-23","arxiv_id":"2405.14559","repositories_listed":1,"syntology":null},{"url":"/paper/hi-gmisnet-generalized-medical-image","slug":"hi-gmisnet-generalized-medical-image","title":"Hi-gMISnet: generalized medical image segmentation using DWT based multilayer fusion and dual mode attention into high resolution pGAN","date":"2024-05-20","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/blackbox-adaptation-for-medical-image","slug":"blackbox-adaptation-for-medical-image","title":"Blackbox Adaptation for Medical Image Segmentation","date":"2024-05-17","arxiv_id":"2405.10913","repositories_listed":1,"syntology":null},{"url":"/paper/cts-a-consistency-based-medical-image","slug":"cts-a-consistency-based-medical-image","title":"CTS: A Consistency-Based Medical Image Segmentation Model","date":"2024-05-15","arxiv_id":"2405.09056","repositories_listed":1,"syntology":null},{"url":"/paper/shape-aware-synthesis-of-pathological-lung-ct","slug":"shape-aware-synthesis-of-pathological-lung-ct","title":"Shape-aware synthesis of pathological lung CT scans using CycleGAN for enhanced semi-supervised lung segmentation","date":"2024-05-14","arxiv_id":"2405.08556","repositories_listed":1,"syntology":null},{"url":"/paper/adaptation-of-distinct-semantics-for","slug":"adaptation-of-distinct-semantics-for","title":"Adaptation of Distinct Semantics for Uncertain Areas in Polyp Segmentation","date":"2024-05-13","arxiv_id":"2405.07523","repositories_listed":1,"syntology":null},{"url":"/paper/emcad-efficient-multi-scale-convolutional","slug":"emcad-efficient-multi-scale-convolutional","title":"EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation","date":"2024-05-11","arxiv_id":"2405.06880","repositories_listed":1,"syntology":{"n":23,"n_ran":20,"n_constructed":5,"n_ran_checked":17,"n_instrument":3,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":16,"n_pointer_only":23,"phrase":"20 ran (of which 5 constructed an object rather than computing a result; 17 with no instrument failure: 1 honoured, 0 violated, 16 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/emcad-efficient-multi-scale-convolutional#ran","syntology_url":"https://syntology.ai/paper/2405.06880","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.06880"}},"official":{"repos":["sldgroup/emcad"],"state":"official (archive's flag): 20 ran","n_ran":20,"n_constructed":5,"n_ran_no_instrument_failure":17,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/modality-agnostic-domain-generalizable","slug":"modality-agnostic-domain-generalizable","title":"Modality-agnostic Domain Generalizable Medical Image Segmentation by Multi-Frequency in Multi-Scale Attention","date":"2024-05-10","arxiv_id":"2405.06284","repositories_listed":1,"syntology":null},{"url":"/paper/pclmix-weakly-supervised-medical-image","slug":"pclmix-weakly-supervised-medical-image","title":"PCLMix: Weakly Supervised Medical Image Segmentation via Pixel-Level Contrastive Learning and Dynamic Mix Augmentation","date":"2024-05-10","arxiv_id":"2405.06288","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-surgical-instrument-segmentation","slug":"enhancing-surgical-instrument-segmentation","title":"Enhancing surgical instrument segmentation: integrating vision transformer insights with adapter","date":"2024-05-08","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/implantable-adaptive-cells-differentiable","slug":"implantable-adaptive-cells-differentiable","title":"Implantable Adaptive Cells: differentiable architecture search to improve the performance of any trained U-shaped network","date":"2024-05-06","arxiv_id":"2405.03420","repositories_listed":1,"syntology":null},{"url":"/paper/on-enhancing-brain-tumor-segmentation-across","slug":"on-enhancing-brain-tumor-segmentation-across","title":"On Enhancing Brain Tumor Segmentation Across Diverse Populations with Convolutional Neural Networks","date":"2024-05-05","arxiv_id":"2405.02852","repositories_listed":1,"syntology":null},{"url":"/paper/raffesdg-random-frequency-filtering-enabled","slug":"raffesdg-random-frequency-filtering-enabled","title":"RaffeSDG: Random Frequency Filtering enabled Single-source Domain Generalization for Medical Image Segmentation","date":"2024-05-02","arxiv_id":"2405.01228","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-bidirectional-displacement-for-semi","slug":"adaptive-bidirectional-displacement-for-semi","title":"Adaptive Bidirectional Displacement for Semi-Supervised Medical Image Segmentation","date":"2024-05-01","arxiv_id":"2405.00378","repositories_listed":1,"syntology":null},{"url":"/paper/crossmatch-enhance-semi-supervised-medical","slug":"crossmatch-enhance-semi-supervised-medical","title":"CrossMatch: Enhance Semi-Supervised Medical Image Segmentation with Perturbation Strategies and Knowledge Distillation","date":"2024-05-01","arxiv_id":"2405.00354","repositories_listed":1,"syntology":null},{"url":"/paper/predictive-accuracy-based-active-learning-for","slug":"predictive-accuracy-based-active-learning-for","title":"Predictive Accuracy-Based Active Learning for Medical Image Segmentation","date":"2024-05-01","arxiv_id":"2405.00452","repositories_listed":1,"syntology":null},{"url":"/paper/a-flexible-2-5d-medical-image-segmentation","slug":"a-flexible-2-5d-medical-image-segmentation","title":"A Flexible 2.5D Medical Image Segmentation Approach with In-Slice and Cross-Slice Attention","date":"2024-04-30","arxiv_id":"2405.00130","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-attention-gated-with-hybrid-dual","slug":"rethinking-attention-gated-with-hybrid-dual","title":"Rethinking Attention Gated with Hybrid Dual Pyramid Transformer-CNN for Generalized Segmentation in Medical Imaging","date":"2024-04-28","arxiv_id":"2404.18199","repositories_listed":1,"syntology":null},{"url":"/paper/glims-attention-guided-lightweight-multi","slug":"glims-attention-guided-lightweight-multi","title":"GLIMS: Attention-Guided Lightweight Multi-Scale Hybrid Network for Volumetric Semantic Segmentation","date":"2024-04-27","arxiv_id":"2404.17854","repositories_listed":1,"syntology":null},{"url":"/paper/auto-generating-weak-labels-for-real","slug":"auto-generating-weak-labels-for-real","title":"Auto-Generating Weak Labels for Real & Synthetic Data to Improve Label-Scarce Medical Image Segmentation","date":"2024-04-25","arxiv_id":"2404.17033","repositories_listed":1,"syntology":null},{"url":"/paper/discriminative-features-pyramid-network-for","slug":"discriminative-features-pyramid-network-for","title":"Discriminative features pyramid network for medical image segmentation","date":"2024-04-25","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/multimodal-information-interaction-for","slug":"multimodal-information-interaction-for","title":"Multimodal Information Interaction for Medical Image Segmentation","date":"2024-04-25","arxiv_id":"2404.16371","repositories_listed":1,"syntology":null},{"url":"/paper/ultrasound-sam-adapter-adapting-sam-for","slug":"ultrasound-sam-adapter-adapting-sam-for","title":"Ultrasound SAM Adapter: Adapting SAM for Breast Lesion Segmentation in Ultrasound Images","date":"2024-04-23","arxiv_id":"2404.14837","repositories_listed":1,"syntology":null},{"url":"/paper/boosting-medical-image-segmentation","slug":"boosting-medical-image-segmentation","title":"Boosting Medical Image Segmentation Performance with Adaptive Convolution Layer","date":"2024-04-17","arxiv_id":"2404.11361","repositories_listed":1,"syntology":null},{"url":"/paper/how-to-build-the-best-medical-image","slug":"how-to-build-the-best-medical-image","title":"How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model","date":"2024-04-15","arxiv_id":"2404.09957","repositories_listed":1,"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":0,"phrase":"0 ran · 3 unverified","sample_list":"/paper/how-to-build-the-best-medical-image#ran","syntology_url":"https://syntology.ai/paper/2404.09957","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.09957"}},"official":{"repos":["mazurowski-lab/finetune-sam"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"url":"/paper/post-training-network-compression-for-3d","slug":"post-training-network-compression-for-3d","title":"Post-Training Network Compression for 3D Medical Image Segmentation: Reducing Computational Efforts via Tucker Decomposition","date":"2024-04-15","arxiv_id":"2404.09683","repositories_listed":1,"syntology":null},{"url":"/paper/constructing-and-exploring-intermediate","slug":"constructing-and-exploring-intermediate","title":"Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image Segmentation","date":"2024-04-13","arxiv_id":"2404.08951","repositories_listed":1,"syntology":{"n":18,"n_ran":17,"n_constructed":0,"n_ran_checked":17,"n_instrument":0,"n_unverified":1,"n_honours":2,"n_violates":0,"n_no_contract":15,"n_pointer_only":7,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 17 with no instrument failure: 2 honoured, 0 violated, 15 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/constructing-and-exploring-intermediate#ran","syntology_url":"https://syntology.ai/paper/2404.08951","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.08951"}},"official":{"repos":["mqinghe/midss"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":0,"n_ran_no_instrument_failure":17,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/lucf-net-lightweight-u-shaped-cascade-fusion","slug":"lucf-net-lightweight-u-shaped-cascade-fusion","title":"LUCF-Net: Lightweight U-shaped Cascade Fusion Network for Medical Image Segmentation","date":"2024-04-11","arxiv_id":"2404.07473","repositories_listed":1,"syntology":null},{"url":"/paper/test-time-adaptation-with-salip-a-cascade-of","slug":"test-time-adaptation-with-salip-a-cascade-of","title":"Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero shot Medical Image Segmentation","date":"2024-04-09","arxiv_id":"2404.06362","repositories_listed":1,"syntology":{"n":12,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":5,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/test-time-adaptation-with-salip-a-cascade-of#ran","syntology_url":"https://syntology.ai/paper/2404.06362","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.06362"}},"official":{"repos":["aleemsidra/SaLIP"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/fpl-filtered-pseudo-label-based-unsupervised","slug":"fpl-filtered-pseudo-label-based-unsupervised","title":"FPL+: Filtered Pseudo Label-based Unsupervised Cross-Modality Adaptation for 3D Medical Image Segmentation","date":"2024-04-07","arxiv_id":"2404.04971","repositories_listed":1,"syntology":null},{"url":"/paper/lhu-net-a-light-hybrid-u-net-for-cost","slug":"lhu-net-a-light-hybrid-u-net-for-cost","title":"LHU-Net: A Light Hybrid U-Net for Cost-Efficient, High-Performance Volumetric Medical Image Segmentation","date":"2024-04-07","arxiv_id":"2404.05102","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/lhu-net-a-light-hybrid-u-net-for-cost#ran","syntology_url":"https://syntology.ai/paper/2404.05102","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.05102"}},"official":{"repos":["xmindflow/lhunet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/litenext-a-novel-lightweight-convmixer-based","slug":"litenext-a-novel-lightweight-convmixer-based","title":"LiteNeXt: A Novel Lightweight ConvMixer-based Model with Self-embedding Representation Parallel for Medical Image Segmentation","date":"2024-04-04","arxiv_id":"2405.15779","repositories_listed":1,"syntology":null},{"url":"/paper/language-guided-domain-generalized-medical","slug":"language-guided-domain-generalized-medical","title":"Language Guided Domain Generalized Medical Image Segmentation","date":"2024-04-01","arxiv_id":"2404.01272","repositories_listed":1,"syntology":null},{"url":"/paper/rsaformer-a-method-of-polyp-segmentation-with","slug":"rsaformer-a-method-of-polyp-segmentation-with","title":"RSAFormer: A method of polyp segmentation with region self-attention transformer","date":"2024-04-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/agileformer-spatially-agile-transformer-unet","slug":"agileformer-spatially-agile-transformer-unet","title":"AgileFormer: Spatially Agile Transformer UNet for Medical Image Segmentation","date":"2024-03-29","arxiv_id":"2404.00122","repositories_listed":1,"syntology":null},{"url":"/paper/medclip-sam-bridging-text-and-image-towards","slug":"medclip-sam-bridging-text-and-image-towards","title":"MedCLIP-SAM: Bridging Text and Image Towards Universal Medical Image Segmentation","date":"2024-03-29","arxiv_id":"2403.20253","repositories_listed":1,"syntology":null},{"url":"/paper/ultralight-vm-unet-parallel-vision-mamba","slug":"ultralight-vm-unet-parallel-vision-mamba","title":"UltraLight VM-UNet: Parallel Vision Mamba Significantly Reduces Parameters for Skin Lesion Segmentation","date":"2024-03-29","arxiv_id":"2403.20035","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/ultralight-vm-unet-parallel-vision-mamba#ran","syntology_url":"https://syntology.ai/paper/2403.20035","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.20035"}},"official":{"repos":["wurenkai/UltraLight-VM-UNet"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/generative-medical-segmentation","slug":"generative-medical-segmentation","title":"Generative Medical Segmentation","date":"2024-03-27","arxiv_id":"2403.18198","repositories_listed":1,"syntology":null},{"url":"/paper/unleashing-the-potential-of-sam-for-medical","slug":"unleashing-the-potential-of-sam-for-medical","title":"Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding","date":"2024-03-27","arxiv_id":"2403.18271","repositories_listed":1,"syntology":{"n":12,"n_ran":9,"n_constructed":3,"n_ran_checked":9,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":2,"phrase":"9 ran (of which 3 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/unleashing-the-potential-of-sam-for-medical#ran","syntology_url":"https://syntology.ai/paper/2403.18271","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.18271"}},"official":{"repos":["cccccczh404/h-sam"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":3,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/segment-any-medical-model-extended","slug":"segment-any-medical-model-extended","title":"Segment Any Medical Model Extended","date":"2024-03-26","arxiv_id":"2403.18114","repositories_listed":1,"syntology":null},{"url":"/paper/3d-effivitcaps-3d-efficient-vision","slug":"3d-effivitcaps-3d-efficient-vision","title":"3D-EffiViTCaps: 3D Efficient Vision Transformer with Capsule for Medical Image Segmentation","date":"2024-03-25","arxiv_id":"2403.16350","repositories_listed":1,"syntology":null},{"url":"/paper/clustering-propagation-for-universal-medical","slug":"clustering-propagation-for-universal-medical","title":"Clustering Propagation for Universal Medical Image Segmentation","date":"2024-03-25","arxiv_id":"2403.16646","repositories_listed":1,"syntology":null},{"url":"/paper/smtf-sparse-transformer-with-multiscale","slug":"smtf-sparse-transformer-with-multiscale","title":"SMTF: Sparse transformer with multiscale contextual fusion for medical image segmentation","date":"2024-03-24","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/matchseg-towards-better-segmentation-via","slug":"matchseg-towards-better-segmentation-via","title":"MatchSeg: Towards Better Segmentation via Reference Image Matching","date":"2024-03-23","arxiv_id":"2403.15901","repositories_listed":1,"syntology":null},{"url":"/paper/anytime-anywhere-anyone-investigating-the","slug":"anytime-anywhere-anyone-investigating-the","title":"Anytime, Anywhere, Anyone: Investigating the Feasibility of Segment Anything Model for Crowd-Sourcing Medical Image Annotations","date":"2024-03-22","arxiv_id":"2403.15218","repositories_listed":1,"syntology":null},{"url":"/paper/towards-a-comprehensive-efficient-and","slug":"towards-a-comprehensive-efficient-and","title":"Towards a Comprehensive, Efficient and Promptable Anatomic Structure Segmentation Model using 3D Whole-body CT Scans","date":"2024-03-22","arxiv_id":"2403.15063","repositories_listed":1,"syntology":{"n":10,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 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; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/towards-a-comprehensive-efficient-and#ran","syntology_url":"https://syntology.ai/paper/2403.15063","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.15063"}},"official":{"repos":["alibaba-damo-academy/ct-sam3d"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/diversified-and-personalized-multi-rater","slug":"diversified-and-personalized-multi-rater","title":"Diversified and Personalized Multi-rater Medical Image Segmentation","date":"2024-03-20","arxiv_id":"2403.13417","repositories_listed":1,"syntology":{"n":13,"n_ran":6,"n_constructed":5,"n_ran_checked":6,"n_instrument":0,"n_unverified":7,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"6 ran (of which 5 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/diversified-and-personalized-multi-rater#ran","syntology_url":"https://syntology.ai/paper/2403.13417","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.13417"}},"official":{"repos":["ycwu1997/d-persona"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":5,"n_ran_no_instrument_failure":6,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/h-vmunet-high-order-vision-mamba-unet-for","slug":"h-vmunet-high-order-vision-mamba-unet-for","title":"H-vmunet: High-order Vision Mamba UNet for Medical Image Segmentation","date":"2024-03-20","arxiv_id":"2403.13642","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":6,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/h-vmunet-high-order-vision-mamba-unet-for#ran","syntology_url":"https://syntology.ai/paper/2403.13642","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.13642"}},"official":{"repos":["wurenkai/h-vmunet"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/morestyle-relax-low-frequency-constraint-of","slug":"morestyle-relax-low-frequency-constraint-of","title":"MoreStyle: Relax Low-frequency Constraint of Fourier-based Image Reconstruction in Generalizable Medical Image Segmentation","date":"2024-03-18","arxiv_id":"2403.11689","repositories_listed":1,"syntology":null},{"url":"/paper/topologically-faithful-multi-class","slug":"topologically-faithful-multi-class","title":"Topologically Faithful Multi-class Segmentation in Medical Images","date":"2024-03-16","arxiv_id":"2403.11001","repositories_listed":1,"syntology":null},{"url":"/paper/d-net-dynamic-large-kernel-with-dynamic","slug":"d-net-dynamic-large-kernel-with-dynamic","title":"D-Net: Dynamic Large Kernel with Dynamic Feature Fusion for Volumetric Medical Image Segmentation","date":"2024-03-15","arxiv_id":"2403.10674","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/d-net-dynamic-large-kernel-with-dynamic#ran","syntology_url":"https://syntology.ai/paper/2403.10674","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.10674"}},"official":{"repos":["sotiraslab/dlk"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/vm-unet-v2-rethinking-vision-mamba-unet-for","slug":"vm-unet-v2-rethinking-vision-mamba-unet-for","title":"VM-UNET-V2 Rethinking Vision Mamba UNet for Medical Image Segmentation","date":"2024-03-14","arxiv_id":"2403.09157","repositories_listed":1,"syntology":null},{"url":"/paper/average-calibration-error-a-differentiable","slug":"average-calibration-error-a-differentiable","title":"Average Calibration Error: A Differentiable Loss for Improved Reliability in Image Segmentation","date":"2024-03-11","arxiv_id":"2403.06759","repositories_listed":1,"syntology":null},{"url":"/paper/shortcut-learning-in-medical-image","slug":"shortcut-learning-in-medical-image","title":"Shortcut Learning in Medical Image Segmentation","date":"2024-03-11","arxiv_id":"2403.06748","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/shortcut-learning-in-medical-image#ran","syntology_url":"https://syntology.ai/paper/2403.06748","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.06748"}},"official":{"repos":["nina-weng/shortcut_skinseg"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/lightm-unet-mamba-assists-in-lightweight-unet","slug":"lightm-unet-mamba-assists-in-lightweight-unet","title":"LightM-UNet: Mamba Assists in Lightweight UNet for Medical Image Segmentation","date":"2024-03-08","arxiv_id":"2403.05246","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":2,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/lightm-unet-mamba-assists-in-lightweight-unet#ran","syntology_url":"https://syntology.ai/paper/2403.05246","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.05246"}},"official":{"repos":["mrblankness/lightm-unet"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/promise-promptable-medical-image-segmentation","slug":"promise-promptable-medical-image-segmentation","title":"ProMISe: Promptable Medical Image Segmentation using SAM","date":"2024-03-07","arxiv_id":"2403.04164","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-weakly-supervised-3d-medical-image","slug":"enhancing-weakly-supervised-3d-medical-image","title":"Enhancing Weakly Supervised 3D Medical Image Segmentation through Probabilistic-aware Learning","date":"2024-03-05","arxiv_id":"2403.02566","repositories_listed":1,"syntology":null},{"url":"/paper/fedlppa-learning-personalized-prompt-and","slug":"fedlppa-learning-personalized-prompt-and","title":"FedLPPA: Learning Personalized Prompt and Aggregation for Federated Weakly-supervised Medical Image Segmentation","date":"2024-02-27","arxiv_id":"2402.17502","repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-medical-image-segmentation-6","slug":"semi-supervised-medical-image-segmentation-6","title":"Semi-supervised Medical Image Segmentation Method Based on Cross-pseudo Labeling Leveraging Strong and Weak Data Augmentation Strategies","date":"2024-02-17","arxiv_id":"2402.11273","repositories_listed":1,"syntology":null},{"url":"/paper/medical-image-segmentation-with-intent","slug":"medical-image-segmentation-with-intent","title":"Medical Image Segmentation with InTEnt: Integrated Entropy Weighting for Single Image Test-Time Adaptation","date":"2024-02-14","arxiv_id":"2402.09604","repositories_listed":1,"syntology":null}],"record_sha256":"693574928ab014ded99bf63310cfc097cbd8b84dbee6728e92e44041ab48e245","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}