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/biomedia-mbzuai/brats2024_biomediambz

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On Enhancing Brain Tumor Segmentation Across Diverse Populations with Convolutional Neural Networks

https://github.com/biomedia-mbzuai/contrastive-echo

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Contrastive Pretraining for Echocardiography Segmentation with Limited Data

https://github.com/biomedia-mbzuai/salt

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SALT: Singular Value Adaptation with Low-Rank Transformation

https://github.com/biomedia-mira/masf

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Domain Generalization via Model-Agnostic Learning of Semantic Features

https://github.com/biomedical-data-analysis-laboratory/adnet-for-ais-segmentation

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Self-Supervised Few-Shot Learning for Ischemic Stroke Lesion Segmentation

https://github.com/blacky-i/MDE_colon_segmentation

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Expanding the Medical Decathlon dataset: segmentation of colon and colorectal cancer from computed tomography images

https://github.com/boguss1225/irissegmentationkeras

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Novel Deep Learning Framework For Bovine Iris Segmentation

https://github.com/boqian333/e2enet-medical

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E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation

https://github.com/bowang-lab/medsam

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Segment Anything in Medical Images

https://github.com/brack-wang/zeco

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ZECO: ZeroFusion Guided 3D MRI Conditional Generation

https://github.com/brainimageanalysis/ici-loss

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Improving Segmentation of Objects with Varying Sizes in Biomedical Images using Instance-wise and Center-of-Instance Segmentation Loss Function

https://github.com/brainlesion/brats

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BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysis

https://github.com/brats-inpainting/2023_challenge

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The Brain Tumor Segmentation (BraTS) Challenge: Local Synthesis of Healthy Brain Tissue via Inpainting

https://github.com/briley-byl123/mper

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Multi-Prototype Embedding Refinement for Semi-Supervised Medical Image Segmentation

https://github.com/browatbn2/VLight

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The Unreasonable Effectiveness of Encoder-Decoder Networks for Retinal Vessel Segmentation

https://github.com/brudfors/spm_superres

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MRI Super-Resolution using Multi-Channel Total Variation

https://github.com/bscs12/bussam

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Ultrasound SAM Adapter: Adapting SAM for Breast Lesion Segmentation in Ultrasound Images

https://github.com/bwgzk/cyclemix

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CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision

https://github.com/bwgzk/shapepu

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ShapePU: A New PU Learning Framework Regularized by Global Consistency for Scribble Supervised Cardiac Segmentation

https://github.com/bwittmann/vesselFM

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vesselFM: A Foundation Model for Universal 3D Blood Vessel Segmentation

https://github.com/bx0903/pdc

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Parameter Decoupling Strategy for Semi-supervised 3D Left Atrium Segmentation

https://github.com/by-liu/confkd

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Segmentation with mixed supervision: Confidence maximization helps knowledge distillation

https://github.com/c-feng/DirectionalFeature

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Learning Directional Feature Maps for Cardiac MRI Segmentation

https://github.com/c-hepburn/Bone_MRI

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Volume of hyperintense inflammation (VHI): a deep learning-enabled quantitative imaging biomarker of inflammation load in spondyloarthritis

https://github.com/cai4cai/ace-dliris

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Average Calibration Error: A Differentiable Loss for Improved Reliability in Image Segmentation

https://github.com/caipengzhou/brau-netplusplus

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BRAU-Net++: U-Shaped Hybrid CNN-Transformer Network for Medical Image Segmentation

https://github.com/camera-mri/spark2024

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Parameter-efficient Fine-tuning for improved Convolutional Baseline for Brain Tumor Segmentation in Sub-Saharan Africa Adult Glioma Dataset

https://github.com/caoluyang0830/cvbm

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Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation

https://github.com/carrierlxk/COSNet

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See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks

https://github.com/ccarliu/m3ae

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M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing Modalities

https://github.com/cccccczh404/h-sam

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Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding

https://github.com/cchen-cc/Robust-Mseg

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Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion

https://github.com/cchen-cc/SIFA

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Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation

https://github.com/cchen-cc/ma-sam

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MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation

https://github.com/cgalaz01/mnms2_challenge

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Tempera: Spatial Transformer Feature Pyramid Network for Cardiac MRI Segmentation

https://github.com/cgpxy/aau-net

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AAU-net: An Adaptive Attention U-net for Breast Lesions Segmentation in Ultrasound Images

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