https://github.com/louisdo2108/acs-nnu-net
공식 코드Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal Embedding
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/lucasfidon/trabit_brats2021
공식 코드https://github.com/lzeeorno/AFFSegNet
공식 코드AFFSegNet: Adaptive Feature Fusion Segmentation Network for Microtumors and Multi-Organ Segmentation
https://github.com/lzeeorno/SMAFormer
공식 코드SMAFormer: Synergistic Multi-Attention Transformer for Medical Image Segmentation
https://github.com/lzeeorno/assnet
공식 코드AFFSegNet: Adaptive Feature Fusion Segmentation Network for Microtumors and Multi-Organ Segmentation
https://github.com/majid9418/eresu-net
공식 코드Enhancing Brain Tumor Segmentation Using Channel Attention and Transfer learning
https://github.com/maloadba/mgenseg_2d
공식 코드https://github.com/manem-lab/lung-ddpm
공식 코드Lung-DDPM: Semantic Layout-guided Diffusion Models for Thoracic CT Image Synthesis
https://github.com/marianocabezas/challenges2018
공식 코드Survival prediction using ensemble tumor segmentation and transfer learning
https://github.com/mateuszbuda/brain-segmentation
공식 코드https://github.com/mathildefaanes/us_brain_tumor_segmentation
공식 코드https://github.com/matt3o/autopet2-submission
공식 코드AutoPET Challenge 2023: Sliding Window-based Optimization of U-Net
https://github.com/mdciri/3D-augmentation-techniques
공식 코드What is the best data augmentation for 3D brain tumor segmentation?
https://github.com/mdciri/Vox2Vox
공식 코드https://github.com/mdciri/augmentation
공식 코드What is the best data augmentation for 3D brain tumor segmentation?
https://github.com/med-air/3dsam-adapter
공식 코드3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation
https://github.com/medcai/l2snet
공식 코드https://github.com/medicl-vu/prism
공식 코드PRISM: A Promptable and Robust Interactive Segmentation Model with Visual Prompts
https://github.com/medicl-vu/variability
공식 코드https://github.com/medimair/brain-mri-processing-pipeline
공식 코드Negligible effect of brain MRI data preprocessing for tumor segmentation
https://github.com/menggerSherry/Trans_Fusion_Net
공식 코드https://github.com/mj129/cipa
공식 코드Cross-Modal Interactive Perception Network with Mamba for Lung Tumor Segmentation in PET-CT Images
https://github.com/mkang315/mctseg
공식 코드https://github.com/mlf-core/mlf-core
공식 코드https://github.com/mobaidoctor/med-ddpm
공식 코드Conditional Diffusion Models for Semantic 3D Brain MRI Synthesis
https://github.com/mobarakol/3D_Attention_UNet
공식 코드Brain Tumor Segmentation and Survival Prediction using 3D Attention UNet
https://github.com/mobarakol/GBM_Prognosis
공식 코드https://github.com/moeinheidari7829/Retroperitoneal_Tumour_Segmentation_SPIE2025
공식 코드A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation
https://github.com/mrgiovanni/continuallearning
공식 코드Continual Learning for Abdominal Multi-Organ and Tumor Segmentation
https://github.com/mrgiovanni/onlinelearning
공식 코드https://github.com/mrgiovanni/synthetictumors
공식 코드https://github.com/muhamadusman/assist
공식 코드Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion models
https://github.com/mungomeng/deepmts
공식 코드https://github.com/mungomeng/survival-adamss
공식 코드https://github.com/mungomeng/survival-deepmss
공식 코드https://github.com/mungomeng/survival-deepmts
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.