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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 621–630 of 786 papers

TitleStatusHype
Robust Pancreatic Ductal Adenocarcinoma Segmentation with Multi-Institutional Multi-Phase Partially-Annotated CT Scans—0
An Ensemble Approach for Brain Tumor Segmentation and Synthesis—0
Robust Tumor Segmentation with Hyperspectral Imaging and Graph Neural Networks—0
Unified HT-CNNs Architecture: Transfer Learning for Segmenting Diverse Brain Tumors in MRI from Gliomas to Pediatric Tumors—0
An End-to-End learnable Flow Regularized Model for Brain Tumor Segmentation—0
Scribble-based Hierarchical Weakly Supervised Learning for Brain Tumor Segmentation—0
United adversarial learning for liver tumor segmentation and detection of multi-modality non-contrast MRI—0
Seeing Beyond Cancer: Multi-Institutional Validation of Object Localization and 3D Semantic Segmentation using Deep Learning for Breast MRI—0
SAR: Scale-Aware Restoration Learning for 3D Tumor Segmentation—0
Segment Anything Model for Brain Tumor Segmentation—0
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