Papers with Code 아카이브

Medical Image Segmentation

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하위 태스크: 31
평가표: 49
보존된 평가 기록: 332

Medical Image Segmentation is a computer vision task that involves dividing an medical image into multiple segments, where each segment represents a different object or structure of interest in the image. The goal of medical image segmentation is to provide a precise and accurate representation of the objects of interest within the image, typically for the purpose of diagnosis, treatment planning, and quantitative analysis.

<span style="color:grey; opacity: 0.6">( Image credit: IVD-Net )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/meclabtuda/med-nca

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Med-NCA: Robust and Lightweight Segmentation with Neural Cellular Automata

https://github.com/med-air/3dsam-adapter

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3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation

https://github.com/med-air/cmc

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Robust Semi-supervised Multimodal Medical Image Segmentation via Cross Modality Collaboration

https://github.com/med-air/dltta

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DLTTA: Dynamic Learning Rate for Test-time Adaptation on Cross-domain Medical Images

https://github.com/med-air/gazemedseg

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Weakly-supervised Medical Image Segmentation with Gaze Annotations

https://github.com/med-air/iop-fl

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IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentation

https://github.com/medcai/l2snet

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A Localization-to-Segmentation Framework for Automatic Tumor Segmentation in Whole-Body PET/CT Images

https://github.com/mediabrain-sjtu/lorkd

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LoRKD: Low-Rank Knowledge Decomposition for Medical Foundation Models

https://github.com/medical-image-analysis-laboratory/ms_wml_uncs

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Structural-Based Uncertainty in Deep Learning Across Anatomical Scales: Analysis in White Matter Lesion Segmentation

https://github.com/medicinetoken/medical-sam2

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Medical SAM 2: Segment medical images as video via Segment Anything Model 2

https://github.com/medicl-vu/cats

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CATS v2: Hybrid encoders for robust medical segmentation

https://github.com/medicl-vu/colossal

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COLosSAL: A Benchmark for Cold-start Active Learning for 3D Medical Image Segmentation

https://github.com/medicl-vu/crossmoda2023

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Learning Site-specific Styles for Multi-institutional Unsupervised Cross-modality Domain Adaptation

https://github.com/medicl-vu/prism-placenta

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Interactive Segmentation Model for Placenta Segmentation from 3D Ultrasound images

https://github.com/medicl-vu/promise

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Promise:Prompt-driven 3D Medical Image Segmentation Using Pretrained Image Foundation Models

https://github.com/medicl-vu/variability

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Assessing Test-time Variability for Interactive 3D Medical Image Segmentation with Diverse Point Prompts

https://github.com/melinphd/taal

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TAAL: Test-time Augmentation for Active Learning in Medical Image Segmentation

https://github.com/menggerSherry/Trans_Fusion_Net

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Exploiting full Resolution Feature Context for Liver Tumor and Vessel Segmentation via Integrate Framework: Application to Liver Tumor and Vessel 3D Reconstruction under embedded microprocessor

https://github.com/mengqi-lei/condseg

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ConDSeg: A General Medical Image Segmentation Framework via Contrast-Driven Feature Enhancement

https://github.com/metamedseg/Code

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MetaMedSeg: Volumetric Meta-learning for Few-Shot Organ Segmentation

https://github.com/mi2datalab/swinsmt

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Swin SMT: Global Sequential Modeling in 3D Medical Image Segmentation

https://github.com/miagrouput/augmentations-for-the-unknown

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Data-Agnostic Augmentations for Unknown Variations: Out-of-Distribution Generalisation in MRI Segmentation

https://github.com/mic-dkfz/autopet-3-submission

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From FDG to PSMA: A Hitchhiker's Guide to Multitracer, Multicenter Lesion Segmentation in PET/CT Imaging

https://github.com/mic-dkfz/miccai2024_autopet3_datacentric

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Data-Centric Strategies for Overcoming PET/CT Heterogeneity: Insights from the AutoPET III Lesion Segmentation Challenge

https://github.com/mic-dkfz/multitalent

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MultiTalent: A Multi-Dataset Approach to Medical Image Segmentation

https://github.com/michaelofsbu/spatialcorrection

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Learning to Segment from Noisy Annotations: A Spatial Correction Approach

https://github.com/michaelvorndran/inconsistencymasks

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Inconsistency Masks: Removing the Uncertainty from Input-Pseudo-Label Pairs

https://github.com/michi-3000/fundus2video

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Fundus2Video: Cross-Modal Angiography Video Generation from Static Fundus Photography with Clinical Knowledge Guidance

https://github.com/miclab-unicamp/medical-imaging-tutorial

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Automated computed tomography and magnetic resonance imaging segmentation using deep learning: a beginner's guide

https://github.com/microsoft/lymphoma-segmentation-dnn

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Comprehensive framework for evaluation of deep neural networks in detection and quantification of lymphoma from PET/CT images: clinical insights, pitfalls, and observer agreement analyses

https://github.com/microsoft/voxhrnet

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Whole Brain Segmentation with Full Volume Neural Network

https://github.com/mikevoets/jama16-retina-replication

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Replication study: Development and validation of deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs

https://github.com/mileswyn/samihs

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SAMIHS: Adaptation of Segment Anything Model for Intracranial Hemorrhage Segmentation

https://github.com/mindflow-institue/daeformer

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DAE-Former: Dual Attention-guided Efficient Transformer for Medical Image Segmentation

https://github.com/mindflow-institue/dermosegdiff

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DermoSegDiff: A Boundary-aware Segmentation Diffusion Model for Skin Lesion Delineation

https://github.com/mindflow-institue/ssct

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Self-supervised Semantic Segmentation: Consistency over Transformation

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.