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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 331–340 of 786 papers

TitleStatusHype
Cross-modal tumor segmentation using generative blending augmentation and self trainingCode0
Unsupervised Brain Tumor Segmentation with Image-based Prompts—0
Medical Image Analysis using Deep Relational Learning—0
Memory-Efficient 3D Denoising Diffusion Models for Medical Image ProcessingCode1
Label-Free Liver Tumor SegmentationCode2
Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging—0
M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing ModalitiesCode1
3D PETCT Tumor Lesion Segmentation via GCN Refinement—0
Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging—0
Multi-class Brain Tumor Segmentation using Graph Attention Network—0
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