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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

Showing 5160 of 436 papers

TitleStatusHype
ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing ModalitiesCode1
CLISC: Bridging clip and sam by enhanced cam for unsupervised brain tumor segmentationCode1
CANet: Context Aware Network for 3D Brain Glioma SegmentationCode1
CAVM: Conditional Autoregressive Vision Model for Contrast-Enhanced Brain Tumor MRI SynthesisCode1
ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly SegmentationCode1
cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image SynthesisCode1
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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