This article discusses the challenges in manual brain tumor segmentation, proposing a region of interest detection algorithm to enhance data preprocessing. By utilizing multiple MRI modalities, a fully convolutional autoencoder with attention mechanisms achieves state-of-the-art segmentation performance on BraTS benchmarks. Test-time augmentations and an energy-based model are employed for uncertainty predictions. The proposed models show significant improvements in segmenting different brain tumor regions, enhancing clinical diagnosis and treatment efficacy.
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by Zachary Schw... at arxiv.org 03-18-2024
https://arxiv.org/pdf/2401.00587.pdfDeeper Inquiries