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arxiv: 2604.12574 · v1 · submitted 2026-04-14 · 💻 cs.CV

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

Francesco Chiumento , Julia Dietlmeier , Ronan P. Killeen , Kathleen M. Curran , Noel E. O'Connor , Mingming Liu This is my paper

Pith reviewed 2026-05-10 14:57 UTC · model grok-4.3

classification 💻 cs.CV
keywords amyloid-beta detectionMRIknowledge distillationAlzheimer's diseasePET-free predictioncross-modal learningneuroimaging
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0 comments X

The pith

A teacher model trained on paired PET and MRI data can distill its knowledge into an MRI-only student that detects amyloid-beta positivity without PET scans or clinical variables.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper aims to make early Alzheimer's screening more accessible by predicting amyloid-beta deposits from routine MRI scans alone. It trains a teacher network on paired PET-MRI images to learn aligned representations through cross-modal attention and contrastive learning. That knowledge then transfers to a student network that sees only MRI inputs via feature and logit distillation. The student reaches AUC scores of 0.74 and 0.68 on two separate test collections while producing saliency maps focused on expected cortical regions. If the transfer succeeds, it removes the cost and availability barriers of PET imaging for population-level use.

Core claim

The authors establish that cross-modal knowledge distillation from a teacher learning PET-MRI alignments enables an MRI-only student to perform amyloid-beta detection with AUC values up to 0.74 on one independent collection and 0.68 on another, across four MRI contrasts, without clinical covariates and with anatomically plausible explanations.

What carries the argument

The PET-guided teacher-student distillation framework, in which the teacher learns cross-modal alignments from paired scans and the student replicates its features and outputs from MRI alone.

If this is right

  • Amyloid-beta status becomes estimable from standard MRI scans without any PET imaging.
  • The method removes the need for clinical covariates at inference time.
  • Interpretability is retained through saliency analysis focused on relevant brain areas.
  • Performance generalizes across multiple MRI sequence types and independent test collections.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar distillation could support prediction of other brain biomarkers from accessible scans.
  • Routine clinical MRI workflows might incorporate this for wider early-risk identification.
  • Validation across more diverse populations would test how broadly the approach applies.

Load-bearing premise

The cross-modal alignments and predictive patterns learned from paired PET-MRI data transfer effectively to standalone MRI inputs without major loss of accuracy or added spurious correlations.

What would settle it

An MRI-only model showing AUC below 0.65 on a new independent collection or producing saliency maps that ignore known amyloid-affected cortical regions would show the transfer has not worked.

Figures

Figures reproduced from arXiv: 2604.12574 by Francesco Chiumento, Julia Dietlmeier, Kathleen M. Curran, Mingming Liu, Noel E. O'Connor, Ronan P. Killeen.

Figure 1
Figure 1. Figure 1: Overview of our PET-guided knowledge distillation framework. A teacher model learns cross-modal alignment between PET and [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Phase 1 — Teacher pre-training with binary classifi￾cation loss (BCE). 4.6. Phase 2: Contrastive Teacher Refinement CL-Aware Online Negative Sampling We perform on￾line hard-negative mining using PET-derived CL values during training. Triplets (anchor, positive, negative) are formed at patient level: anchor and positive from the same subject/session (PET+) to enforce intra-session con￾sistency, while negat… view at source ↗
Figure 3
Figure 3. Figure 3: Phase 2 — Teacher refinement with online CL-aware triplet mining. 4.7. Phase 3: Knowledge Distillation MarginFocal Loss We use margin-augmented focal loss with margin-shifted logits z˜i = zi + m(1 − 2ˆyi), where yˆi = 1[y ′ i > 1/2] and y ′ i are the (optionally smoothed) targets: \label {eq:mf} \begin {aligned} \mathcal {L}_{\mathrm {MF}} &= \frac {1}{N}\sum _{i=1}^N (1-p_{t_i})^{\gamma }\,\mathrm {BCE}_w… view at source ↗
Figure 4
Figure 4. Figure 4: Phase 3 — Knowledge distillation. MRI-only student is trained via ℓ2 feature matching and temperature-scaled logit dis￾tillation; PET/CL are used only for teacher supervision. 4.8. Distillation Loss Function Our training combines three losses (Eqs. (5), (6), (7)): \label {eq:distillation-loss} \begin {split} \mathcal {L}_{\text {total}} = &\lambda _{\text {cls}} \mathcal {L}_{\mathrm {MF}} \\ &+ \lambda _{… view at source ↗
Figure 5
Figure 5. Figure 5: ROC curves per sequence. (a) OASIS-3; (b) ADNI. FLAIR and T2* show moderate discrimination, likely due to smaller sample sizes and weaker structural correlates, yet remain useful at θ ∗ for high-recall screening. Comparison with Prior Work [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Saliency/HiResCAM. OASIS-3, epochs 1\to 25 . 5.3. Cross-Dataset Transfer [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: compares models that use a single sequence with models that use multiple sequences and are then tested on a single sequence using five metrics (F1, Accuracy, Precision, Recall, AUC). Multi-sequence models consistently improve recall (e.g., T1w on OASIS-3: 0.64 → 0.71, +10.9%), while also increasing AUC on OASIS-3 (0.73 → 0.74) and ac￾curacy for T1w on ADNI (0.50 → 0.56), and maintaining comparable precisio… view at source ↗
Figure 8
Figure 8. Figure 8: ROC curve evolution across datasets and sequences. Validation performance at Epoch 1 (initialization) vs. Final Epoch (convergence). Top row: OASIS-3 dataset for T1w+T2w training (left) and FLAIR+T2* training (right). Bottom row: ADNI dataset with same training configurations. All models show substantial AUC improvements demonstrating effective knowledge distillation. 17 [PITH_FULL_IMAGE:figures/full_fig_… view at source ↗
Figure 9
Figure 9. Figure 9: Single-sequence training: attention evolution across datasets. Training progression (epochs 1, 8, 25) for models trained on individual MRI contrasts. Each row displays PET reference, target MRI, gradient saliency, and HiResCAM maps. The network progressively focuses on anatomically relevant brain structures, with consistent patterns across OASIS-3 and ADNI datasets demonstrating robust generalization. 18 … view at source ↗
Figure 10
Figure 10. Figure 10: Multi-sequence training with single-sequence inference. Saliency/HiResCAM evolution (epochs 1, 8, 25) for models trained on paired sequences (T1w+T2w, FLAIR+T2*) and tested on individual contrasts, showing consistent spatial attention patterns across modalities. 19 [PITH_FULL_IMAGE:figures/full_fig_p019_10.png] view at source ↗
read the original abstract

Detecting amyloid-$\beta$ (A$\beta$) 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 A$\beta$ 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 A$\beta$ screening. Code is available at https://github.com/FrancescoChiumento/pet-guided-mri-amyloid-detection.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript proposes a PET-guided knowledge distillation framework for amyloid-beta positivity prediction from MRI alone. A BiomedCLIP-based teacher learns cross-modal PET-MRI alignment via attention and Centiloid-aware triplet contrastive learning with online negative sampling; an MRI-only student then mimics the teacher through feature- and logit-level distillation. The approach is evaluated on four MRI contrasts (T1w, T2w, FLAIR, T2*) across OASIS-3 and ADNI, reporting peak AUCs of 0.74 and 0.68, with saliency maps focused on cortical regions. The work emphasizes elimination of PET and clinical covariates at inference while providing open code.

Significance. If the knowledge-transfer mechanism is shown to be effective, the result would enable more accessible Aβ screening using routine MRI, addressing a clear clinical need. The open-source code and focus on interpretability are strengths that support potential adoption and extension. However, the current empirical evidence does not yet establish that the reported AUCs derive from the PET-informed components rather than standard MRI classification.

major comments (3)
  1. [Abstract / Results] Abstract and Results: The AUC values (0.74 on OASIS-3, 0.68 on ADNI) are reported without any baseline comparisons to supervised MRI-only models, non-distilled students, or alternative distillation strategies, nor ablations that remove the Centiloid-aware contrastive term or cross-modal attention. This directly undermines the central claim that the framework demonstrates effective PET-guided knowledge transfer.
  2. [Methods] Methods: No quantitative validation of representation transfer is provided, such as CKA similarity, Procrustes alignment, or feature correlations computed on held-out paired PET-MRI cases. Without such metrics, it remains possible that the student simply learns generic MRI patterns, rendering the teacher’s PET-informed representations unnecessary.
  3. [Experiments] Experiments: Dataset split details, statistical significance tests, confidence intervals or error bars on the AUCs, and per-contrast performance breakdowns are absent. These omissions prevent assessment of whether the reported numbers are robust or generalizable across the two independent cohorts.
minor comments (2)
  1. [Abstract] Abstract: The statement 'best AUC' should explicitly identify the MRI contrast and model configuration that achieves the quoted numbers.
  2. [Results] The saliency analysis is mentioned but the precise method (e.g., Grad-CAM variant) and quantitative overlap with known Aβ-vulnerable regions could be stated more precisely in the main text.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback, which identifies key areas where additional validation will strengthen the manuscript. We address each major comment below and will revise the paper to incorporate the requested elements.

read point-by-point responses

  1. Referee: [Abstract / Results] Abstract and Results: The AUC values (0.74 on OASIS-3, 0.68 on ADNI) are reported without any baseline comparisons to supervised MRI-only models, non-distilled students, or alternative distillation strategies, nor ablations that remove the Centiloid-aware contrastive term or cross-modal attention. This directly undermines the central claim that the framework demonstrates effective PET-guided knowledge transfer.

    Authors: We agree that baseline comparisons are necessary to isolate the contribution of the PET-guided components. In the revised manuscript we will add: (i) results from supervised MRI-only models trained from scratch on the same task, (ii) performance of the student model without any distillation, and (iii) ablations that remove the Centiloid-aware triplet contrastive term and the cross-modal attention mechanism. These will be reported alongside the original AUCs in the Results section with the same evaluation protocol. revision: yes

  2. Referee: [Methods] Methods: No quantitative validation of representation transfer is provided, such as CKA similarity, Procrustes alignment, or feature correlations computed on held-out paired PET-MRI cases. Without such metrics, it remains possible that the student simply learns generic MRI patterns, rendering the teacher’s PET-informed representations unnecessary.

    Authors: We acknowledge the value of direct quantitative evidence of representation transfer. We will compute and report Centered Kernel Alignment (CKA) similarities as well as Pearson correlations between corresponding teacher and student feature maps on held-out paired PET-MRI cases. These metrics will be added to the Methods or supplementary material to demonstrate that the student is aligning with the teacher’s PET-informed representations rather than learning generic MRI features. revision: yes

  3. Referee: [Experiments] Experiments: Dataset split details, statistical significance tests, confidence intervals or error bars on the AUCs, and per-contrast performance breakdowns are absent. These omissions prevent assessment of whether the reported numbers are robust or generalizable across the two independent cohorts.

    Authors: We apologize for these omissions. The revised manuscript will include: detailed train/validation/test split sizes and stratification criteria for both OASIS-3 and ADNI; DeLong’s test p-values for AUC comparisons; 95% confidence intervals obtained via bootstrapping for all AUCs; and a complete per-contrast performance table (T1w, T2w, FLAIR, T2*) for each dataset. These additions will allow readers to evaluate robustness and cross-cohort generalizability. revision: yes

Circularity Check

0 steps flagged

No significant circularity: empirical ML pipeline with independent experimental validation

full rationale

The paper describes a standard teacher-student knowledge distillation framework for MRI-based amyloid prediction, trained on paired PET-MRI data and evaluated via AUC on held-out datasets (OASIS-3, ADNI). No equations, parameters, or predictions are defined in terms of themselves or reduced to fitted inputs by construction. The central claims rest on empirical performance metrics and saliency maps rather than any self-citation chain, uniqueness theorem, or ansatz that imports the result. This is a purely data-driven pipeline whose validity can be checked against external benchmarks without circular reduction.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The claim rests on standard deep-learning assumptions plus the availability of paired PET-MRI training data; no new physical entities are introduced.

free parameters (1)
  • Distillation loss weights and contrastive temperature
    Typical hyperparameters in knowledge-distillation frameworks; not enumerated in abstract.
axioms (2)
  • domain assumption Paired PET-MRI data exists and is sufficient to learn transferable cross-modal representations
    Invoked by the teacher training stage described in the abstract.
  • domain assumption Saliency maps on cortical regions indicate genuine clinical relevance rather than dataset artifacts
    Used to support interpretability claim.

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discussion (0)

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