跨模态医学影像分割新模型，提升精度与可解释性
Modality-Projection Universal Model for Comprehensive Full-Body Medical Imaging Segmentation
- 通过动态调参的模态投影策略，适配多种医学影像
- 在全身体域影像分割中实现高精度结构识别
- 支持多层显著性图可视化，便于临床解读
深度学习在医学影像中的应用已展现出提升诊断、治疗和研究结果的巨大潜力。然而，由于数据特征的固有差异，将通用模型应用于多种影像模态仍面临挑战。本研究提出并评估了一种模态投影通用模型（MPUM）。MPUM采用新颖的模态投影策略，能够动态调整参数以优化不同成像模态下的性能。MPUM在识别解剖结构方面表现出色，支持精确量化，有助于改善临床决策。同时，它还能揭示脑-体轴内的代谢关联，推动对脑-体生理相关性的研究。此外，其基于控制器的卷积层可生成全网络层的显著性图，显著增强模型可解释性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。