mlsanityData to 14 Oct 2026

Cross-Modal Knowledge Distillation for PET-Free Amyloid-Beta Detection from MRI

arXiv 2604.12574 ↗First posted 14 Apr 2026cs.CVcited by 0

On the map: Knowledge distillation → Distillation for medical imaging

Abstract (arXiv)

Detecting amyloid-β (Aβ) positivity is crucial for early diagnosis of Alzheimer's disease but typically requires PET imaging, which is costly, invasive, and not widely accessible, limiting its use for population-level screening. We address this gap by proposing a PET-guided knowledge distillation framework that enables Aβ prediction from MRI alone, without requiring non-imaging clinical covariates or PET at inference. Our approach employs a BiomedCLIP-based teacher model that learns PET-MRI alignment via cross-modal attention and triplet contrastive learning with PET-informed (Centiloid-aware) online negative sampling. An MRI-only student then mimics the teacher via feature-level and logit-level distillation. Evaluated across four MRI contrasts (T1w, T2w, FLAIR, T2*) and two independent datasets, our approach demonstrates effective knowledge transfer (best AUC: 0.74 on OASIS-3, 0.68 on ADNI) while maintaining interpretability and eliminating the need for clinical variables. Saliency analysis confirms that predictions focus on anatomically relevant cortical regions, supporting the clinical viability of PET-free Aβ screening. Code is available at https://github.com/FrancescoChiumento/pet-guided-mri-amyloid-detection.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Distillation for medical imaging, one of 12 directions in Knowledge distillation. Builds on 10 of its 59 references that are in the map. First posted on arXiv 14 Apr 2026.

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10 of 59 references.

References · 10

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  2. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  3. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
  4. 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,371
  5. 2019Automated brain extraction of multi-sequence MRI using artificial neural networkscited by 134
  6. 2020Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networkscited by 79
  7. 2019The reliability of a deep learning model in clinical out-of-distribution MRI data: a multicohort studycited by 38
  8. 2018Reproducible evaluation of classification methods in Alzheimer's disease: framework and application to MRI and PET datacited by 14
  9. 2024Multimodal MRI Accurately Identifies Amyloid Status in Unbalanced Cohorts in Alzheimer's Disease Continuumcited by 2
  10. 2024Deep Learning-Based Prediction of PET Amyloid Status Using Multi-Contrast MRIcited by 1

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