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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 351–360 of 436 papers

TitleStatusHype
One-pass Multi-task Networks with Cross-task Guided Attention for Brain Tumor SegmentationCode0
Optimized U-Net for Brain Tumor SegmentationCode0
Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain AdaptationCode0
FR-MRInet: A Deep Convolutional Encoder-Decoder for Brain Tumor Segmentation with Relu-RGB and Sliding-windowCode0
Optimizing Medical Image Segmentation with Advanced Decoder DesignCode0
HMM Model for Brain Tumor Detection and ClassificationCode0
Distributionally Robust Deep Learning using Hardness Weighted SamplingCode0
Hybrid-Fusion Transformer for Multisequence MRICode0
Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural NetworksCode0
FMG-Net and W-Net: Multigrid Inspired Deep Learning Architectures For Medical Imaging SegmentationCode0
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