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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 641–650 of 786 papers

TitleStatusHype
A Generalized Deep Learning Framework for Whole-Slide Image Segmentation and AnalysisCode1
Transfer Learning for Brain Tumor Segmentation—0
Robustness of Brain Tumor Segmentation—0
Domain Knowledge Based Brain Tumor Segmentation and Overall Survival PredictionCode0
Multimodal Self-Supervised Learning for Medical Image Analysis—0
The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 ChallengeCode1
Multi-Resolution 3D CNN for MRI Brain Tumor Segmentation and Survival Prediction—0
Weakly Supervised Fine Tuning Approach for Brain Tumor Segmentation ProblemCode0
Scribble-based Hierarchical Weakly Supervised Learning for Brain Tumor Segmentation—0
Semantic Feature Attention Network for Liver Tumor Segmentation in Large-scale CT database—0
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