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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://git.fmrib.ox.ac.uk/vaanathi/truenet_tumseg

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Brain tumour segmentation using a triplanar ensemble of U-Nets

https://github.com/920232796/nestedformer

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NestedFormer: Nested Modality-Aware Transformer for Brain Tumor Segmentation

https://github.com/AKuzina/DWP

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Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems

https://github.com/Aarhus-RadOnc-AI/MedSAM_multimodality_HNC_GTV_segmentation

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Segment anything model for head and neck tumor segmentation with CT, PET and MRI multi-modality images

https://github.com/Arksyd96/synthesis-with-slice-based-ldm

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3D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving Tumor Segmentation Tasks in Data-Scarce Regimes

https://github.com/Beckschen/3D-TransUNet

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3D TransUNet: Advancing Medical Image Segmentation through Vision Transformers

https://github.com/CAMERA-MRI/SPARK2023/tree/main/SPARK_BTS_KIFARU

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Generative Style Transfer for MRI Image Segmentation: A Case of Glioma Segmentation in Sub-Saharan Africa

https://github.com/CUMC-Yuan-Lab/GCML/tree/main/SANet/BraTS

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Automatic Brain Tumor Segmentation with Scale Attention Network

https://github.com/CancerDataScience/NuCLS

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

https://github.com/China-LiuXiaopeng/BraTS-DMFNet

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3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRI

https://github.com/ChrisMats/CSAW-S

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Adding Seemingly Uninformative Labels Helps in Low Data Regimes

https://github.com/ChrisMats/seemingly_uninformative_labels

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Adding Seemingly Uninformative Labels Helps in Low Data Regimes

https://github.com/Confusezius/unet-lits-2d-pipeline

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Liver Lesion Segmentation with slice-wise 2D Tiramisu and Tversky loss function

https://github.com/FETS-AI/Challenge

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The Federated Tumor Segmentation (FeTS) Challenge

https://github.com/FETS-AI/Front-End

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The Federated Tumor Segmentation (FeTS) Challenge

https://github.com/Guo-Xiaoqing/BraTS_OS

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Domain Knowledge Based Brain Tumor Segmentation and Overall Survival Prediction

https://github.com/HealthML/self-supervised-3d-tasks

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3D Self-Supervised Methods for Medical Imaging

https://github.com/HiLab-git/SSL4MIS

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All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation

https://github.com/IBBM/Cascaded-FCN

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

https://github.com/JoetheReindeer/multiPI_TransBTS

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multiPI-TransBTS: A Multi-Path Learning Framework for Brain Tumor Image Segmentation Based on Multi-Physical Information

https://github.com/JohnleeHIT/Brats2019

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Multi-step Cascaded Networks for Brain Tumor Segmentation

https://github.com/KeleiHe/BiGL

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Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain Adaptation

https://github.com/LINGYUNFDU/MSSU-Net

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Multi-Scale Supervised 3D U-Net for Kidneys and Kidney Tumor Segmentation

https://github.com/Luc4Gui/CAVM

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CAVM: Conditional Autoregressive Vision Model for Contrast-Enhanced Brain Tumor MRI Synthesis

https://github.com/LucasFidon/GeneralizedWassersteinDiceLoss

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Generalized Wasserstein Dice Score, Distributionally Robust Deep Learning, and Ranger for brain tumor segmentation: BraTS 2020 challenge

https://github.com/LucasFidon/HardnessWeightedSampler

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Distributionally Robust Deep Learning using Hardness Weighted Sampling

https://github.com/MIC-DKFZ/nnunet

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nnU-Net for Brain Tumor Segmentation

https://github.com/MJJdG/ULS23

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The ULS23 Challenge: a Baseline Model and Benchmark Dataset for 3D Universal Lesion Segmentation in Computed Tomography

https://github.com/MarvinLer/tcga_segmentation

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Weakly supervised multiple instance learning histopathological tumor segmentation

https://github.com/NVIDIA/DeepLearningExamples

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Optimized U-Net for Brain Tumor Segmentation

https://github.com/NikooMoradi/HNTSMRG24_team_TUMOR

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Comparative Analysis of nnUNet and MedNeXt for Head and Neck Tumor Segmentation in MRI-guided Radiotherapy

https://github.com/Precision-Medical-Imaging-Group/BraTS2023-inferCode

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Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance Imaging

https://github.com/Precision-Medical-Imaging-Group/BraTS2024-PEDS

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

https://github.com/Project-MONAI/MONAI

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Automated head and neck tumor segmentation from 3D PET/CT

https://github.com/RL4M/PCRLv2

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PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysis

https://github.com/RanSuLab/RAUNet-tumor-segmentation

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RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans

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