Papers with Code 아카이브

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/KrissHs/KIT19

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

https://github.com/LIVIAETS/boundary-loss

Esophageal Tumor Segmentation in CT Images using Dilated Dense Attention Unet (DDAUnet)

https://github.com/LIVIAETS/surface-loss

Esophageal Tumor Segmentation in CT Images using Dilated Dense Attention Unet (DDAUnet)

https://github.com/Luffy03/FreeTumor

FreeTumor: Advance Tumor Segmentation via Large-Scale Tumor Synthesis

https://github.com/Mind23-2/MindCode-101/tree/main/nnUNet

nnU-Net for Brain Tumor Segmentation

https://github.com/MindCode-4/code-9/tree/main/smu

SMU-Net: Style matching U-Net for brain tumor segmentation with missing modalities

https://github.com/MindSpore-scientific/code-13/tree/main/Translation-Invariant/model

Translation Consistent Semi-supervised Segmentation for 3D Medical Images

https://github.com/MindSpore-scientific/code-5/tree/main/smu

SMU-Net: Style matching U-Net for brain tumor segmentation with missing modalities

https://github.com/MissShihongHowRU/two-stage-VAE-Attention-gate-BraTS2020

A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor Segmentation

https://github.com/PathologyDataScience/NuCLS

NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentation

https://github.com/Precision-Medical-Imaging-Group/HOPE-Segmenter-Kids

Magnetic Resonance Imaging Feature-Based Subtyping and Model Ensemble for Enhanced Brain Tumor Segmentation

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

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

https://github.com/RobinRajSB/Brain-Tumor-Segmentation-Using-CNN

Brain Tumor Segmentation with Deep Neural Networks

https://github.com/SWKoreaBME/sangwook_kits2019

Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge

https://github.com/SeyedMuhammadHosseinMousavi/Neural-Gas-Network-Image-Features-and-Segmentation-for-Brain-Tumor-Detection-Using-MRI-Data

Neural Gas Network Image Features and Segmentation for Brain Tumor Detection Using Magnetic Resonance Imaging Data

https://github.com/Shrajan/IB_U_Nets

3D MRI brain tumor segmentation using autoencoder regularization

https://github.com/Stsh4lson/TOM-2020

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

https://github.com/WouterDurnez/airhead

nnU-Net for Brain Tumor Segmentation

https://github.com/YuanXue1993/SegAN

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

https://github.com/abhi134/Brain_Tumor_Segmentation

Brain Tumor Segmentation with Deep Neural Networks

https://github.com/aimansnigdha/ambiguous-medical-image-segmentation-using-diffusion-models

Diffusion Models for Implicit Image Segmentation Ensembles

https://github.com/ajinas-ibrahim/brain_tumor

Brain Tumor Segmentation with Deep Neural Networks

https://github.com/andreped/livermask

The Liver Tumor Segmentation Benchmark (LiTS)

https://github.com/angadbajwa23/Brain-Image-Segmentation

Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge

https://github.com/angadbajwa23/Brain-Tumor-Segmentation

Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge

https://github.com/angadbajwa23/Segmentation-of-2D-Brain-MR-Images-using-Deep-Neural-Architectures

Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge

https://github.com/aryan-satpathy/backdoor

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

https://github.com/assassint2017/MICCAI-LITS2017

The Liver Tumor Segmentation Benchmark (LiTS)

https://github.com/athon2/BraTS2018_NvNet

3D MRI brain tumor segmentation using autoencoder regularization

https://github.com/avinash0309/BRAIN-TUMOR-SEGEMENTATION-OF-LOW-GRADE-GLIOMAS

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

https://github.com/black0017/MedicalZooPytorch

3D MRI brain tumor segmentation using autoencoder regularization

https://github.com/charan223/Brain-Tumor-Segmentation-using-Topological-Loss

Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks

https://github.com/charan223/brain_tumor_topology

Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks

https://github.com/charan223/topology-aware-networks

Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks

https://github.com/charan223/topology-conscious-networks

Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks

https://github.com/christophbrgr/brats-orchestra

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

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