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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 1120 of 436 papers

TitleStatusHype
GBT-SAM: Adapting a Foundational Deep Learning Model for Generalizable Brain Tumor Segmentation via Efficient Integration of Multi-Parametric MRI DataCode1
CLISC: Bridging clip and sam by enhanced cam for unsupervised brain tumor segmentationCode1
XLSTM-HVED: Cross-Modal Brain Tumor Segmentation and MRI Reconstruction Method Using Vision XLSTM and Heteromodal Variational Encoder-DecoderCode1
cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image SynthesisCode1
Optimizing Brain Tumor Segmentation with MedNeXt: BraTS 2024 SSA and PediatricsCode1
Enhanced MRI brain tumor detection and classification via topological data analysis and low-rank tensor decompositionCode1
Multiscale Encoder and Omni-Dimensional Dynamic Convolution Enrichment in nnU-Net for Brain Tumor SegmentationCode1
Fed-MUnet: Multi-modal Federated Unet for Brain Tumor SegmentationCode1
SimTxtSeg: Weakly-Supervised Medical Image Segmentation with Simple Text CuesCode1
Unsupervised Domain Adaptation for Pediatric Brain Tumor SegmentationCode1
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