Public articles linked to the same research event.
Journal of Zhejiang University(Science Edition) Using ADNI data, this study builds a patient-level multimodal fusion framework based on ResNet50 transfer learning that concatenates MRI and PET imaging features with clinical variables (ADAS11, ADAS13, APOE4, age, sex, education, MMSE total) in a fully connected network to produce three-way AD/MCI/CN predictions, with Grad-CAM heatmaps for explanation; the ablation shows accuracy rising as modalities are added, from 39.61% for MRI alone and 51.72% for PET alone to 66.67% for clinical-only, 54.17% for MRI+PET, and 79.17% for the full three-modality model on a 24-patient test cohort (95% Wilson interval roughly 59.5%-90.8%), with the addition of clinical data producing the single largest gain.
Using ADNI data, this study builds a patient-level multimodal fusion framework based on ResNet50 transfer learning that concatenates MRI and PET imaging features with clinical variables (ADAS11, ADAS13, APOE4, age, sex, education, MMSE total) in a fully connected network to produce three-way AD/MCI/CN predictions, with Grad-CAM heatmaps for explanation; the ablation shows accuracy rising as modalities are added, from 39.61% for MRI alone and 51.72% for PET alone to 66.67% for clinical-only, 54.17% for MRI+PET, and 79.17% for the full three-modality model on a 24-patient test cohort (95% Wilson interval roughly 59.5%-90.8%), with the addition of clinical data producing the single largest gain.
Using ADNI data, this study builds a patient-level multimodal fusion framework based on ResNet50 transfer learning that concatenates MRI and PET imaging features with clinical variables (ADAS11, ADAS13, APOE4, age, sex, education, MMSE total) in a fully connected network to produce three-way AD/MCI/CN predictions, with Grad-CAM heatmaps for explanation; the ablation shows accuracy rising as modalities are added, from 39.61% for MRI alone and 51.72% for PET alone to 66.67% for clinical-only, 54.17% for MRI+PET, and 79.17% for the full three-modality model on a 24-patient test cohort (95% Wilson interval roughly 59.5%-90.8%), with the addition of clinical data producing the single largest gain.
Using ADNI data, this study builds a patient-level multimodal fusion framework based on ResNet50 transfer learning that concatenates MRI and PET imaging features with clinical variables (ADAS11, ADAS13, APOE4, age, sex, education, MMSE total) in a fully connected network to produce three-way AD/MCI/CN predictions, with Grad-CAM heatmaps for explanation; the ablation shows accuracy rising as modalities are added, from 39.61% for MRI alone and 51.72% for PET alone to 66.67% for clinical-only, 54.17% for MRI+PET, and 79.17% for the full three-modality model on a 24-patient test cohort (95% Wilson interval roughly 59.5%-90.8%), with the addition of clinical data producing the single largest gain.