Diffusion-Refined Segmentation and Vision-Language Interpretation for Pediatric Brain Tumor MRI

arXiv:2606.14072v1 Announce Type: cross Abstract: Accurate pediatric brain tumor segmentation remains challenging due to limited annotated data, heterogeneous imaging phenotypes, diffuse tumor boundaries, and class imbalance across tumor subregions. Here, we present a two-stage deep learning framework for improving multi-modal pediatric brain MRI segmentation and clinical interpretation. First, we evaluate 3D Res U-Net and Swin-UNETR baselines on BraTS-PEDs MRI scans, using four co-registered modalities to predict tumor core, whole tumor, and enhancing tumor regions. Second, we introduce diffu

Source: arXiv cs.CL — read the full report at the original publisher.

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