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/neheller/kits23

The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challenge

https://github.com/nesonn/3D-cnn

MRI Tumor Segmentation with Densely Connected 3D CNN

https://github.com/oliverrensu/mvg

U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation

https://github.com/pashtari/factorizer-isles22

Factorizer: A Scalable Interpretable Approach to Context Modeling for Medical Image Segmentation

https://github.com/perslev/Autofocus-Layer-TF

Autofocus Layer for Semantic Segmentation

https://github.com/peteraugustine/seg3

Brain Tumor Segmentation with Deep Neural Networks

https://github.com/pykao/BraTS2018-tumor-segmentation

Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction

https://github.com/pykao/Modified-3D-UNet-Pytorch

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

https://github.com/rubics-xuan/transbts

TransBTS: Multimodal Brain Tumor Segmentation Using Transformer

https://github.com/segis95/BRATS_Segmentation

Weakly Supervised Fine Tuning Approach for Brain Tumor Segmentation Problem

https://github.com/shalabh147/Brain-Tumor-Segmentation-and-Survival-Prediction-using-Deep-Neural-Networks

Brain Tumor Segmentation with Deep Neural Networks

https://github.com/sinclairjang/3D-MRI-brain-tumor-segmentation-using-autoencoder-regularization

3D MRI brain tumor segmentation using autoencoder regularization

https://github.com/soribadiaby/Deep-Learning-liver-segmentation

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

https://github.com/sudohainguyen/STAN-small-tumor-aware-network

BUSIS: A Benchmark for Breast Ultrasound Image Segmentation

https://github.com/syedsajidhussain/3D-GAN

Vox2Vox: 3D-GAN for Brain Tumour Segmentation

https://github.com/taigw/brats18_docker

Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks

https://github.com/taigw/geodesic_distance

DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation

https://github.com/taojlu/Liver-and-Tumor-Segmentation-Papers-List

Automatic Liver Segmentation from CT Images Using Deep Learning Algorithms: A Comparative Study

https://github.com/tbymiracle/Brain-Tumor-Segmentation-Paddle

Brain Tumor Segmentation with Deep Neural Networks

https://github.com/tobiasforest/mimics/tree/main/TBraTS_Zou_MICCAI2022

TBraTS: Trusted Brain Tumor Segmentation

https://github.com/trungnhanuchiha/VAE_BRATS2018

3D MRI brain tumor segmentation using autoencoder regularization

https://github.com/vaibhav253/Enhancement-of-Brain-MRI-Tumor-Segmentation-using-Multimodality-Learning

Multi-channel MRI Embedding: An EffectiveStrategy for Enhancement of Human Brain WholeTumor Segmentation

https://github.com/wenhui0206/Robust-TransBTS

TransBTS: Multimodal Brain Tumor Segmentation Using Transformer

https://github.com/xandery-geek/BackdoorAttacks

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

https://github.com/xmengli999/H-DenseUNet

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

https://github.com/yangyucheng000/-acn_mindspore

ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing Modalities

https://github.com/yangyucheng000/Paper-3/tree/main/msBraVL-master

Brain Tumor Segmentation with Deep Neural Networks

https://github.com/yangyucheng000/Papers/tree/main/STAN

Stan: Small tumor-aware network for breast ultrasound image segmentation

https://github.com/zhyjsiat/a-two-stage-cyclegan-ve-brats2020

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

https://github.com/zz10001/LITS2017-main1

The Liver Tumor Segmentation Benchmark (LiTS)

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