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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 351–360 of 436 papers

TitleStatusHype
Segmentation of brain tumor on magnetic resonance imaging using a convolutional architecture—0
DDU-Nets: Distributed Dense Model for 3D MRI Brain Tumor Segmentation—0
Brain Tumor Segmentation by Cascaded Deep Neural Networks Using Multiple Image Scales—0
Distributionally Robust Deep Learning using Hardness Weighted SamplingCode0
Transfer Learning for Brain Tumor Segmentation—0
Robustness of Brain Tumor Segmentation—0
Domain Knowledge Based Brain Tumor Segmentation and Overall Survival PredictionCode0
Multimodal Self-Supervised Learning for Medical Image Analysis—0
Multi-Resolution 3D CNN for MRI Brain Tumor Segmentation and Survival Prediction—0
Scribble-based Hierarchical Weakly Supervised Learning for Brain Tumor Segmentation—0
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