[2603.04811] Meta-D: Metadata-Aware Architectures for Brain Tumor Analysis and Missing-Modality Segmentation

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.04811 (cs) [Submitted on 5 Mar 2026] Title:Meta-D: Metadata-Aware Architectures for Brain Tumor Analysis and Missing-Modality Segmentation Authors:SangHyuk Kim, Daniel Haehn, Sumientra Rampersad View a PDF of the paper titled Meta-D: Metadata-Aware Architectures for Brain Tumor Analysis and Missing-Modality Segmentation, by SangHyuk Kim and 2 other authors View PDF HTML (experimental) Abstract:We present Meta-D, an architecture that explicitly leverages categorical scanner metadata such as MRI sequence and plane orientation to guide feature extraction for brain tumor analysis. We aim to improve the performance of medical image deep learning pipelines by integrating explicit metadata to stabilize feature representations. We first evaluate this in 2D tumor detection, where injecting sequence (e.g., T1, T2) and plane (e.g., axial) metadata dynamically modulates convolutional features, yielding an absolute increase of up to 2.62% in F1-score over image-only baselines. Because metadata grounds feature extraction when data are available, we hypothesize it can serve as a robust anchor when data are missing. We apply this to 3D missing-modality tumor segmentation. Our Transformer Maximizer utilizes metadata-based cross-attention to isolate and route available modalities, ensuring the network focuses on valid slices. This targeted attention improves brain tumor segmentation Dice scores by up to 5.12% under extrem...