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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 431–440 of 786 papers

TitleStatusHype
Prediction of brain tumor recurrence location based on multi-modal fusion and nonlinear correlation learning—0
Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging—0
FMG-Net and W-Net: Multigrid Inspired Deep Learning Architectures For Medical Imaging SegmentationCode0
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
Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging—0
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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