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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 361–370 of 436 papers

TitleStatusHype
Disentangled Multimodal Brain MR Image Translation via Transformer-based Modality Infuser—0
Does anatomical contextual information improve 3D U-Net based brain tumor segmentation?—0
Domain Game: Disentangle Anatomical Feature for Single Domain Generalized Segmentation—0
Efficient Brain Tumor Segmentation Using a Dual-Decoder 3D U-Net with Attention Gates (DDUNet)—0
Efficient embedding network for 3D brain tumor segmentation—0
Election of Collaborators via Reinforcement Learning for Federated Brain Tumor Segmentation—0
Empirical Evaluation of the Segment Anything Model (SAM) for Brain Tumor Segmentation—0
End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks—0
Enhancing Brain Tumor Classification Using TrAdaBoost and Multi-Classifier Deep Learning Approaches—0
Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation—0
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