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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 211–220 of 786 papers

TitleStatusHype
Diff-Ensembler: Learning to Ensemble 2D Diffusion Models for Volume-to-Volume Medical Image Translation—0
Improving the U-Net Configuration for Automated Delineation of Head and Neck Cancer on MRICode0
Generative Style Transfer for MRI Image Segmentation: A Case of Glioma Segmentation in Sub-Saharan AfricaCode0
KMD: Koopman Multi-modality Decomposition for Generalized Brain Tumor Segmentation under Incomplete Modalities—0
SuperLightNet: Lightweight Parameter Aggregation Network for Multimodal Brain Tumor SegmentationCode0
Incomplete Multi-modal Brain Tumor Segmentation via Learnable Sorting State Space Model—0
Diff4MMLiTS: Advanced Multimodal Liver Tumor Segmentation via Diffusion-Based Image Synthesis and Alignment—0
Election of Collaborators via Reinforcement Learning for Federated Brain Tumor Segmentation—0
A fuzzy rank-based ensemble of CNN models for MRI segmentationCode0
Recommender Engine Driven Client Selection in Federated Brain Tumor Segmentation—0
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