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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 51–60 of 436 papers

TitleStatusHype
Hybrid Window Attention Based Transformer Architecture for Brain Tumor SegmentationCode1
Inter-slice Context Residual Learning for 3D Medical Image SegmentationCode1
Knowledge Distillation for Brain Tumor SegmentationCode1
Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restorationCode1
ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly SegmentationCode1
ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing ModalitiesCode1
Models Genesis: Generic Autodidactic Models for 3D Medical Image AnalysisCode1
MRI brain tumor segmentation and uncertainty estimation using 3D-UNet architecturesCode1
A Joint Graph and Image Convolution Network for Automatic Brain Tumor SegmentationCode1
AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain TumorCode1
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