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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 141–150 of 436 papers

TitleStatusHype
MBDRes-U-Net: Multi-Scale Lightweight Brain Tumor Segmentation NetworkCode0
FedRef: Communication-Efficient Bayesian Fine Tuning with Reference ModelCode0
Automatic Brain Tumor Segmentation with Scale Attention NetworkCode0
FMG-Net and W-Net: Multigrid Inspired Deep Learning Architectures For Medical Imaging SegmentationCode0
Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural NetworksCode0
Adaptive Active Contour Model for Brain Tumor SegmentationCode0
FR-MRInet: A Deep Convolutional Encoder-Decoder for Brain Tumor Segmentation with Relu-RGB and Sliding-windowCode0
Decoupling Feature Representations of Ego and Other Modalities for Incomplete Multi-modal Brain Tumor SegmentationCode0
Automatic brain tumor segmentation in 2D intra-operative ultrasound images using MRI tumor annotationsCode0
Exploiting Partial Common Information Microstructure for Multi-Modal Brain Tumor SegmentationCode0
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