Modality-Projection Universal Model for Comprehensive Full-Body Medical Imaging Segmentation
This work addresses the problem of modality variability in medical imaging for clinicians and researchers, offering an incremental improvement with a novel projection strategy.
The study tackled the challenge of applying universal models across multiple medical imaging modalities by introducing the Modality Projection Universal Model (MPUM), which dynamically adjusts parameters and demonstrated superior accuracy in anatomical segmentation and improved interpretability through saliency maps.
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.