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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 651–660 of 786 papers

TitleStatusHype
Domain Knowledge Based Brain Tumor Segmentation and Overall Survival PredictionCode0
Multimodal Self-Supervised Learning for Medical Image Analysis—0
Multi-Resolution 3D CNN for MRI Brain Tumor Segmentation and Survival Prediction—0
Scribble-based Hierarchical Weakly Supervised Learning for Brain Tumor Segmentation—0
Weakly Supervised Fine Tuning Approach for Brain Tumor Segmentation ProblemCode0
Semantic Feature Attention Network for Liver Tumor Segmentation in Large-scale CT database—0
Modified U-Net (mU-Net) with Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images—0
MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep LearningCode0
Attention Enriched Deep Learning Model for Breast Tumor Segmentation in Ultrasound ImagesCode0
Organ At Risk Segmentation with Multiple Modality—0
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