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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 101–110 of 786 papers

TitleStatusHype
A Generalized Deep Learning Framework for Whole-Slide Image Segmentation and AnalysisCode1
mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor SegmentationCode1
CSC-PA: Cross-image Semantic Correlation via Prototype Attentions for Single-network Semi-supervised Breast Tumor SegmentationCode1
Multi-Modality Generative Adversarial Networks with Tumor Consistency Loss for Brain MR Image SynthesisCode1
Multi-Scale Supervised 3D U-Net for Kidneys and Kidney Tumor SegmentationCode1
Multi-View Hypercomplex Learning for Breast Cancer ScreeningCode1
BUSIS: A Benchmark for Breast Ultrasound Image SegmentationCode1
CT Liver Segmentation via PVT-based Encoding and Refined DecodingCode1
Annotation-efficient deep learning for automatic medical image segmentationCode1
MMOTU: A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic SegmentationCode1
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