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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 331–340 of 436 papers

TitleStatusHype
3D Self-Supervised Methods for Medical ImagingCode1
A Review on End-To-End Methods for Brain Tumor Segmentation and Overall Survival Prediction—0
Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation ProblemsCode0
Neural Architecture Search for Gliomas Segmentation on Multimodal Magnetic Resonance ImagingCode1
An Inductive Transfer Learning Approach using Cycle-consistent Adversarial Domain Adaptation with Application to Brain Tumor Segmentation—0
DeepSeg: Deep Neural Network Framework for Automatic Brain Tumor Segmentation using Magnetic Resonance FLAIR ImagesCode1
Decentralized Differentially Private Segmentation with PATE—0
Arbitrary Scale Super-Resolution for Brain MRI ImagesCode0
Attention-Guided Version of 2D UNet for Automatic Brain Tumor SegmentationCode1
Deep Learning-Based Concurrent Brain Registration and Tumor SegmentationCode1
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