Enhancing the reliability of Alzheimer's disease prediction in MRI images.
Authors
Affiliations (6)
Affiliations (6)
- Department of Mechanical Engineering, National Central University, Taoyuan, Taiwan.
- Department of Electrical Engineering, Yuan Ze University, Taoyuan, Taiwan.
- School of Electronics Engineering, Vellore Institute of Technology, Chennai, India.
- Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung, Taiwan.
- National Center for High-performance Computing, National Applied Research Laboratories, Hsinchu, Taiwan.
- Department of Mechanical Engineering, National Central University, Taoyuan, Taiwan. Electronic address: [email protected].
Abstract
Alzheimer's Disease (AD) diagnostic procedures employing Magnetic Resonance Imaging (MRI) analysis encounter considerable obstacles pertaining to reliability and accuracy, especially when deep learning models are utilized within clinical environments. Present deep learning methodologies for MRI-based AD detection frequently demonstrate spatial dependencies and exhibit deficiencies in robust validation mechanisms. Extant validation techniques inadequately integrate anatomical knowledge and exhibit challenges in feature interpretability across a range of imaging conditions. To address this fundamental gap, we introduce a reverse validation paradigm that systematically repositions anatomical structures to test whether models recognize features based on anatomical characteristics rather than spatial memorization. Our research endeavors to rectify these shortcomings by proposing three innovative methodologies: Feature Position Invariance (FPI) for the validation of anatomical features, biomarker location augmentation aimed at enhancing spatial learning, and High-Confidence Cohort (HCC) selection for the reliable identification of training samples. The FPI methodology leverages reverse validation approach to substantiate model predictions through the reconstruction of anatomical features, bolstered by our extensive data augmentation strategy and a confidence-based sample selection technique. The application of this framework utilizing YOLO and MobileNet architecture has yielded significant advancements in both binary and three-class AD classification tasks, achieving state-of-the-art accuracy with enhancements of 2-4 % relative to baseline models. Additionally, our methodology generates interpretable insights through anatomy-aligned validation, establishing direct links between model decisions and neuropathological features. Our experimental findings reveal consistent performance across various anatomical presentations, signifying that the framework effectively enhances both the reliability and interpretability of AD diagnosis through MRI analysis, thereby equipping medical professionals with a more robust diagnostic support system.