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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 81–90 of 786 papers

TitleStatusHype
SFusion: Self-attention based N-to-One Multimodal Fusion BlockCode1
PA-Seg: Learning from Point Annotations for 3D Medical Image Segmentation using Contextual Regularization and Cross Knowledge DistillationCode1
High-Resolution Swin Transformer for Automatic Medical Image SegmentationCode1
Single MR Image Super-Resolution using Generative Adversarial NetworkCode1
MMOTU: A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic SegmentationCode1
TBraTS: Trusted Brain Tumor SegmentationCode1
mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor SegmentationCode1
FedMix: Mixed Supervised Federated Learning for Medical Image SegmentationCode1
Preoperative brain tumor imaging: models and software for segmentation and standardized reportingCode1
Multi-View Hypercomplex Learning for Breast Cancer ScreeningCode1
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