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Tumor Segmentation

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평가표: 4
보존된 평가 기록: 5

Tumor Segmentation is the task of identifying the spatial location of a tumor. It is a pixel-level prediction where each pixel is classified as a tumor or background. The most popular benchmark for this task is the BraTS dataset. The models are typically evaluated with the Dice Score metric.

코드

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

https://github.com/harveerar/PSIGAN

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PSIGAN: Joint probabilistic segmentation and image distribution matching for unpaired cross-modality adaptation based MRI segmentation

https://github.com/hazardfy/fiba

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FIBA: Frequency-Injection based Backdoor Attack in Medical Image Analysis

https://github.com/hellopipu/TC-MGAN

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Multi-Modality Generative Adversarial Networks with Tumor Consistency Loss for Brain MR Image Synthesis

https://github.com/hiest/distmis

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Distributing Deep Learning Hyperparameter Tuning 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/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/huaibei-normal-university-cv-laboratory/mbdresunet

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MBDRes-U-Net: Multi-Scale Lightweight Brain Tumor Segmentation Network

https://github.com/huangyjhust/3D-RU-Net

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3D RoI-aware U-Net for Accurate and Efficient Colorectal Tumor Segmentation

https://github.com/huiimin5/aua

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Exploring Feature Representation Learning for Semi-supervised Medical Image Segmentation

https://github.com/huiimin5/comwin

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Compete to Win: Enhancing Pseudo Labels for Barely-supervised Medical Image Segmentation

https://github.com/i-sahajmistry/nnunet_brats2023

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Multiscale Encoder and Omni-Dimensional Dynamic Convolution Enrichment in nnU-Net for Brain Tumor Segmentation

https://github.com/iantsen/hecktor

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Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT Images

https://github.com/imatge-upc/mri-braintumor-segmentation

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MRI brain tumor segmentation and uncertainty estimation using 3D-UNet architectures

https://github.com/ispamm/phbreast

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Multi-View Hypercomplex Learning for Breast Cancer Screening

https://github.com/ivadomed/ivadomed

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ivadomed: A Medical Imaging Deep Learning Toolbox

https://github.com/jordan-colman/DR-Unet104

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DR-Unet104 for Multimodal MRI brain tumor segmentation

https://github.com/juliawolleb/diffusion-based-segmentation

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Diffusion Models for Implicit Image Segmentation Ensembles

https://github.com/juntongkuki/pytorch-dsfnet

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DSFNet: Dual-GCN and Location-fused Self-attention with Weighted Fast Normalized Fusion for Polyps Segmentation

https://github.com/junzengz/rmamamba

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A Reverse Mamba Attention Network for Pathological Liver Segmentation

https://github.com/jupitern52/textbrats

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TextBraTS: Text-Guided Volumetric Brain Tumor Segmentation with Innovative Dataset Development and Fusion Module Exploration

https://github.com/justatinydot/bitr-unet

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BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor Segmentation

https://github.com/jwicaksana/fedmix

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FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation

https://github.com/kk42yy/demoseg

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Decoupling Feature Representations of Ego and Other Modalities for Incomplete Multi-modal Brain Tumor Segmentation

https://github.com/koriavinash1/BioExp

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Demystifying Brain Tumour Segmentation Networks: Interpretability and Uncertainty Analysis

https://github.com/kurtlabuw/re-diffinet

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Re-DiffiNet: Modeling discrepancies in tumor segmentation using diffusion models

https://github.com/lab-smile/brainsegfounder

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BrainSegFounder: Towards 3D Foundation Models for Neuroimage Segmentation

https://github.com/lab-smile/gatorbrain

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BrainSegFounder: Towards 3D Foundation Models for Neuroimage Segmentation

https://github.com/lachinov/brats2019

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Knowledge Distillation for Brain Tumor Segmentation

https://github.com/lelechen63/MRI-tumor-segmentation-Brats

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MRI Tumor Segmentation with Densely Connected 3D CNN

https://github.com/lescientifik/open_brats2020

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Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution

https://github.com/lich0031/AIDE

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Annotation-efficient deep learning for automatic medical image segmentation

https://github.com/lingchm/medassist-liver-cancer

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SmoothSegNet: A Global-Local Framework for Liver Tumor Segmentation with Clinical KnowledgeInformed Label Smoothing

https://github.com/linzy0227/pdminet

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Prototype-Driven and Multi-Expert Integrated Multi-Modal MR Brain Tumor Image Segmentation

https://github.com/lkpengcs/syntumoranalyzer

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Optimizing Synthetic Data for Enhanced Pancreatic Tumor Segmentation

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