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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 341–350 of 436 papers

TitleStatusHype
Brain tumor segmentation with missing modalities via latent multi-source correlation representation—0
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
Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated FusionCode1
Knowledge Distillation for Brain Tumor SegmentationCode1
Brain Tumor Segmentation by Cascaded Deep Neural Networks Using Multiple Image Scales—0
Distributionally Robust Deep Learning using Hardness Weighted SamplingCode0
Robust Semantic Segmentation of Brain Tumor Regions from 3D MRIsCode1
Transfer Learning for Brain Tumor Segmentation—0
Robustness of Brain Tumor Segmentation—0
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