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Tumor Segmentation

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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/cocofeat/devis

TBraTS: Trusted Brain Tumor Segmentation

https://github.com/cocofeat/umis

TBraTS: Trusted Brain Tumor Segmentation

https://github.com/code-implementation1/Code9/tree/main/Unet

H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes

https://github.com/dijju/mri-cnn

Brain Tumor Segmentation with Deep Neural Networks

https://github.com/enochkan/vox2vox

Vox2Vox: 3D-GAN for Brain Tumour Segmentation

https://github.com/gallegi/T4E_MICCAI_BrainTumor

The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification

https://github.com/ge-xing/segmamba

The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

https://github.com/iNLyze/DeepLearning-SeGAN-Segmentation

SegAN: Adversarial Network with Multi-scale $L_1$ Loss for Medical Image Segmentation

https://github.com/ikboljon/hecktor_midl_unetr

Automatic Segmentation of Head and Neck Tumor: How Powerful Transformers Are?

https://github.com/ikboljon/tmss_miccai

TMSS: An End-to-End Transformer-based Multimodal Network for Segmentation and Survival Prediction

https://github.com/jadevaibhav/Brain-Tumor-Segmentation-using-Deep-Neural-networks

Brain Tumor Segmentation with Deep Neural Networks

https://github.com/jianpengz/ConResNet

Inter-slice Context Residual Learning for 3D Medical Image Segmentation

https://github.com/jianpengz/DoDNet

DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasets

https://github.com/julianbertini/MSNet

Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks

https://github.com/kidswithtokens/medsegdiff

MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model

https://github.com/kirangpcet/KRCCTumor

The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes

https://github.com/klin059/lits

Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks

https://github.com/koriavinash1/DigitalHistoPath

A Generalized Deep Learning Framework for Whole-Slide Image Segmentation and Analysis

https://github.com/lachinov/brats2018-graphlabunn

Glioma Segmentation with Cascaded Unet

https://github.com/lee-zq/3DUNet-Pytorch

The Liver Tumor Segmentation Benchmark (LiTS)

https://github.com/littleMMJ/mindsporemodels

MRI Tumor Segmentation with Densely Connected 3D CNN

https://github.com/ljollans/trunet

3D TransUNet: Advancing Medical Image Segmentation through Vision Transformers

https://github.com/lsqshr/AH-Net

3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes

https://github.com/lucas-rbnt/bm-mae

The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification

https://github.com/luvgold/auotofoucus3D-Brats

Autofocus Layer for Semantic Segmentation

https://github.com/marrlab/dispr

Diffusion Models for Implicit Image Segmentation Ensembles

https://github.com/mateuszbuda/brain-segmentation-pytorch

Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm

https://github.com/maxiao0234/ttga

Medical Image Segmentation Using Squeeze-and-Expansion Transformers

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

PRISM: A Promptable and Robust Interactive Segmentation Model with Visual Prompts

https://github.com/mehrdad-noori/brain-tumor-segmentation

Attention-Guided Version of 2D UNet for Automatic Brain Tumor Segmentation

https://github.com/michal-crosta/GIT19

The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes

https://github.com/milliondegree/semantic-segmentation-tensorflow

Brain Tumor Segmentation Based on Refined Fully Convolutional Neural Networks with A Hierarchical Dice Loss

https://github.com/mlf-core/liver-ct-segmentation

The Liver Tumor Segmentation Benchmark (LiTS)

https://github.com/mnsv73/brain-tumour-segmentation

Brain Tumor Segmentation with Deep Neural Networks

https://github.com/mobaidoctor/polyp-ddpm

Conditional Diffusion Models for Semantic 3D Brain MRI Synthesis

https://github.com/naldeborgh7575/brain_segmentation

Brain Tumor Segmentation with Deep Neural Networks

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