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/hidneuron/3d-effivitcaps

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3D-EffiViTCaps: 3D Efficient Vision Transformer with Capsule for Medical Image Segmentation

https://github.com/hiest/distmis

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Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation

https://github.com/hilab-git/dag4mia

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CDDSA: Contrastive Domain Disentanglement and Style Augmentation for Generalizable Medical Image Segmentation

https://github.com/hilab-git/dca-net

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Domain Composition and Attention for Unseen-Domain Generalizable Medical Image Segmentation

https://github.com/hilab-git/fpl-plus

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FPL+: Filtered Pseudo Label-based Unsupervised Cross-Modality Adaptation for 3D Medical Image Segmentation

https://github.com/hilab-git/pa-seg

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PA-Seg: Learning from Point Annotations for 3D Medical Image Segmentation using Contextual Regularization and Cross Knowledge Distillation

https://github.com/hilab-git/pf-net

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Semi-Supervised Segmentation of Radiation-Induced Pulmonary Fibrosis from Lung CT Scans with Multi-Scale Guided Dense Attention

https://github.com/hilab-git/pymic

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PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation

https://github.com/hilab-git/pymic_examples

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PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation

https://github.com/hilab-git/segrap2023

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SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma

https://github.com/hilab-git/srpl-sfda

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SRPL-SFDA: SAM-Guided Reliable Pseudo-Labels for Source-Free Domain Adaptation in Medical Image Segmentation

https://github.com/hilab-git/ugtst

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An Uncertainty-guided Tiered Self-training Framework for Active Source-free Domain Adaptation in Prostate Segmentation

https://github.com/hilab-git/upl-sfda

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UPL-SFDA: Uncertainty-aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation

https://github.com/hilab-git/word

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WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image

https://github.com/himashi92/Duo-SegNet

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Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentation

https://github.com/himashi92/lofihippseg

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Bilateral Hippocampi Segmentation in Low Field MRIs Using Mutual Feature Learning via Dual-Views

https://github.com/himashi92/vizviva_brats_2021

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Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation Task

https://github.com/himashi92/vizviva_fets_2022

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Hybrid Window Attention Based Transformer Architecture for Brain Tumor Segmentation

https://github.com/himashi92/vt-unet

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A Robust Volumetric Transformer for Accurate 3D Tumor Segmentation

https://github.com/hitachinsk/samed

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Customized Segment Anything Model for Medical Image Segmentation

https://github.com/hitachinsk/tha

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A Dataset for Deep Learning-based Bone Structure Analyses in Total Hip Arthroplasty

https://github.com/hiyoochan/sequentialtransfer

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Selecting the Best Sequential Transfer Path for Medical Image Segmentation with Limited Labeled Data

https://github.com/hkervadec/shape_descriptors

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Beyond pixel-wise supervision for segmentation: A few global shape descriptors might be surprisingly good!

https://github.com/holgerroth/3Dunet_abdomen_cascade

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An application of cascaded 3D fully convolutional networks for medical image segmentation

https://github.com/hongkunsun/paratranscnn

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ParaTransCNN: Parallelized TransCNN Encoder for Medical Image Segmentation

https://github.com/horsepurve/StyleSegor

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Neural Style Transfer Improves 3D Cardiovascular MR Image Segmentation on Inconsistent Data

https://github.com/hossein1387/U-Net-Fixed-Point-Quantization-for-Medical-Image-Segmentation

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U-Net Fixed-Point Quantization for Medical Image Segmentation

https://github.com/hritam-98/MFSNet

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MFSNet: A Multi Focus Segmentation Network for Skin Lesion Segmentation

https://github.com/hritam-98/gfda-disentangled

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Semi-supervised Domain Adaptive Medical Image Segmentation through Consistency Regularized Disentangled Contrastive Learning

https://github.com/hritam-98/ict-medseg

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An Embarrassingly Simple Consistency Regularization Method for Semi-Supervised Medical Image Segmentation

https://github.com/hrlblab/glo-in-one_v2

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Glo-In-One-v2: Holistic Identification of Glomerular Cells, Tissues, and Lesions in Human and Mouse Histopathology

https://github.com/hrlblab/molecularel

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Democratizing Pathological Image Segmentation with Lay Annotators via Molecular-empowered Learning

https://github.com/hsiangyuzhao/PANet

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Prior Attention Network for Multi-Lesion Segmentation in Medical Images

https://github.com/hsiangyuzhao/adler

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AdLER: Adversarial Training with Label Error Rectification for One-Shot Medical Image Segmentation

https://github.com/hsiangyuzhao/rcps

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RCPS: Rectified Contrastive Pseudo Supervision for Semi-Supervised Medical Image Segmentation

https://github.com/hszhao/PSPNet

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Pyramid Scene Parsing Network

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