Stage-Specific Hybrid ROI Subset Learning for MRI - Based Alzheimer's Diagnosis
29th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2026, Fuzhou, Çin, 13 - 15 Mayıs 2026, ss.2424-2429, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/cscwd68734.2026.11582192
- Basıldığı Şehir: Fuzhou
- Basıldığı Ülke: Çin
- Sayfa Sayıları: ss.2424-2429
- Anahtar Kelimeler: Alzheimer's disease, deep learning, hybrid learning, magnetic resonance imaging, region of interest
- Dokuz Eylül Üniversitesi Adresli: Evet
Özet
Accurate MRI-based differentiation of Alzheimer's disease (AD), mild cognitive impairment (MCI), and cognitively normal (CN) individuals remains challenging due to subtle stage-dependent atrophy patterns and variability across imaging sites. This work proposes a stage-specific hybrid ROI-subset learning framework that decomposes the diagnostic task into three binary problems and discovers compact, discriminative, and non-redundant anatomical region subsets. T1-weighted MRIs from the ADNI1 cohort were preprocessed using standardized N4 correction, skull stripping, nonlinear registration, and atlas parcellation (Harvard- Oxford and Desikan-Killiany). Fixed-size 3D patches extracted from each ROI were encoded using a contrastively pretrained 3D ResNet to obtain harmonized region-level embeddings. A multi-objective sequential selection strategy, namely optimizing discrimination, minimizing redundancy, and achieving subset compactness, identified stable task-specific ROI groups. Lightweight calibrated classifiers were trained under a fully nested cross-validation scheme to eliminate information leakage. The proposed method achieved AUCs of 0.98 (AD vs. CN), 0.84 (AD vs. MCI), and 0.91 (MCI vs. CN), outperforming whole-brain 3D CNNs, transformers, and ROI-based baselines while significantly reducing inference cost. Cross-atlas consistency and frozen-parameter validation on AIBL demonstrated robust anatomical reproducibility and strong transportability. These results highlight compact ROI-subset learning as an efficient and interpretable alternative to end-to-end volumetric deep learning for MRI-based Alzheimer's assessment.