mlsanityData to 14 Oct 2026

Modality-Projection Universal Model for Comprehensive Full-Body Medical Imaging Segmentation

arXiv 2412.19026 ↗First posted 26 Dec 2024eess.IV cs.AI cs.CVcited by 0

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Abstract (arXiv)

The integration of deep learning in medical imaging has shown great promise for enhancing diagnostic, therapeutic, and research outcomes. However, applying universal models across multiple modalities remains challenging due to the inherent variability in data characteristics. This study aims to introduce and evaluate a Modality Projection Universal Model (MPUM). MPUM employs a novel modality-projection strategy, which allows the model to dynamically adjust its parameters to optimize performance across different imaging modalities. The MPUM demonstrated superior accuracy in identifying anatomical structures, enabling precise quantification for improved clinical decision-making. It also identifies metabolic associations within the brain-body axis, advancing research on brain-body physiological correlations. Furthermore, MPUM's unique controller-based convolution layer enables visualization of saliency maps across all network layers, significantly enhancing the model's interpretability.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 37 of its 96 references that are in the map. First posted on arXiv 26 Dec 2024.

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References · 37

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25 more
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