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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 151–160 of 786 papers

TitleStatusHype
Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated FusionCode1
Knowledge Distillation for Brain Tumor SegmentationCode1
Stan: Small tumor-aware network for breast ultrasound image segmentationCode1
Robust Semantic Segmentation of Brain Tumor Regions from 3D MRIsCode1
A Generalized Deep Learning Framework for Whole-Slide Image Segmentation and AnalysisCode1
The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 ChallengeCode1
Demystifying Brain Tumour Segmentation Networks: Interpretability and Uncertainty AnalysisCode1
The Liver Tumor Segmentation Benchmark (LiTS)Code1
Lesion Focused Super-ResolutionCode1
Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 ChallengeCode1
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