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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 61–70 of 436 papers

TitleStatusHype
Abstracting Deep Neural Networks into Concept Graphs for Concept Level InterpretabilityCode1
Diffusion Models for Implicit Image Segmentation EnsemblesCode1
Attention U-Net: Learning Where to Look for the PancreasCode1
E1D3 U-Net for Brain Tumor Segmentation: Submission to the RSNA-ASNR-MICCAI BraTS 2021 ChallengeCode1
Attention-Guided Version of 2D UNet for Automatic Brain Tumor SegmentationCode1
AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain TumorCode1
A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor SegmentationCode1
A Robust Volumetric Transformer for Accurate 3D Tumor SegmentationCode1
Ensemble CNN Networks for GBM Tumors Segmentation using Multi-parametric MRICode1
CANet: Context Aware Network for 3D Brain Glioma SegmentationCode1
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