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Brain Tumor Segmentation

Brain Tumor Segmentation is a medical image analysis task that involves the separation of brain tumors from normal brain tissue in magnetic resonance imaging (MRI) scans. The goal of brain tumor segmentation is to produce a binary or multi-class segmentation map that accurately reflects the location and extent of the tumor.

( Image credit: Brain Tumor Segmentation with Deep Neural Networks )

Papers

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Showing 291–300 of 436 papers

TitleStatusHype
Brain MRI Tumor Segmentation with Adversarial Networks—0
Transfer learning for automatic brain tumor classification Using MRI Images.—0
Transfer Learning for Brain Tumor Segmentation—0
Transfer Learning in Magnetic Resonance Brain Imaging: a Systematic Review—0
TuNet: End-to-end Hierarchical Brain Tumor Segmentation using Cascaded Networks—0
Two-Stage Approach for Brain MR Image Synthesis: 2D Image Synthesis and 3D Refinement—0
Two-stage MR Image Segmentation Method for Brain Tumors based on Attention Mechanism—0
Uncertainty-driven refinement of tumor-core segmentation using 3D-to-2D networks with label uncertainty—0
Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation—0
Unified HT-CNNs Architecture: Transfer Learning for Segmenting Diverse Brain Tumors in MRI from Gliomas to Pediatric Tumors—0
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