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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
Lung-DDPM: Semantic Layout-guided Diffusion Models for Thoracic CT Image SynthesisCode1
AutoPET Challenge 2023: Sliding Window-based Optimization of U-NetCode1
A Robust Volumetric Transformer for Accurate 3D Tumor SegmentationCode1
Automatic Tumor Segmentation via False Positive Reduction Network for Whole-Body Multi-Modal PET/CT ImagesCode1
Rethinking Brain Tumor Segmentation from the Frequency Domain PerspectiveCode1
BUSIS: A Benchmark for Breast Ultrasound Image SegmentationCode1
mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor SegmentationCode1
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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