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Tumor Segmentation

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.

Papers

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Showing 61–70 of 786 papers

TitleStatusHype
ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing ModalitiesCode1
FedMix: Mixed Supervised Federated Learning for Medical Image SegmentationCode1
CAT: Coordinating Anatomical-Textual Prompts for Multi-Organ and Tumor SegmentationCode1
Compete to Win: Enhancing Pseudo Labels for Barely-supervised Medical Image SegmentationCode1
Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 ChallengeCode1
Brain Tumor Segmentation and Survival Prediction using 3D Attention UNetCode1
BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor SegmentationCode1
3D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving Tumor Segmentation Tasks in Data-Scarce RegimesCode1
BrainSegFounder: Towards 3D Foundation Models for Neuroimage SegmentationCode1
Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion modelsCode1
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