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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 111–120 of 436 papers

TitleStatusHype
SEDNet: Shallow Encoder-Decoder Network for Brain Tumor SegmentationCode0
Development of RLK-Unet: a clinically favorable deep learning algorithm for brain metastasis detection and treatment response assessmentCode0
Fully Automated Tumor Segmentation for Brain MRI data using Multiplanner UNet—0
Using Singular Value Decomposition in a Convolutional Neural Network to Improve Brain Tumor Segmentation Accuracy—0
Integrating Edges into U-Net Models with Explainable Activation Maps for Brain Tumor Segmentation using MR Images—0
Brain Tumor Segmentation Based on Deep Learning, Attention Mechanisms, and Energy-Based Uncertainty PredictionCode0
Towards SAMBA: Segment Anything Model for Brain Tumor Segmentation in Sub-Sharan African Populations—0
E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image SegmentationCode1
Automated 3D Tumor Segmentation using Temporal Cubic PatchGAN (TCuP-GAN)—0
End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks—0
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