ResNet-50 on BreastMNIST (published comparison)
ResNet-50 @28px, 1-channel stem, trained from random initialisation
Part of QSMPC-QKD-QHE-AI-Hybrid,
a quantum-safe orchestration demo. This is the plaintext model for the
medical_fl use case; the encrypted path runs a distilled student, not this model.
Measured performance
| metric | value |
|---|---|
accuracy |
0.782051282051282 |
auc |
0.7987677527151211 |
best_val_auc |
0.935673 |
macro_f1 |
0.6492063492063492 |
n_parameters |
23505858 |
resolution |
28 |
wall_clock_s |
11.2 |
Published baselines this is measured against
- Target metric: AUC
- Baseline to beat: 0.857 - ResNet-50 @28px, MedMNIST v2, Yang et al., Scientific Data 10:41 (2023) (AUC 0.857 / ACC 0.812). Note this is BELOW the ResNet-18 baseline of 0.901 on the same corpus and the same 100-epoch Adam regime: on 780 images the deeper network loses. That is the published finding, and it is why this model exists here - to reproduce it rather than to win.
- Published ceiling: 0.919 - Google AutoML Vision, MedMNIST v2 (AUC 0.919)
- Companion metric shown alongside:
accuracy- reported together because the aggregate figure can look healthy while the class that matters is not.
Training data
- Dataset: MedMNIST v2 BreastMNIST
- Licence: CC-BY-4.0
- Source: https://medmnist.com/ (licence read 2026-08-03)
780 images at 28x28. The tiny tier; also the source of the FHE-feasible T1 student.
Notes and limitations
Trained to test a published claim rather than to win one. MedMNIST v2 reports ResNet-50 at AUC 0.857 against ResNet-18's 0.901 on this corpus, so the expected outcome is that the larger model scores LOWER, and it is reported either way. It replaces a DenseNet-121 entry that named a dataset with no loader, no fetch command and no artefact directory, and linked a Hugging Face repo that did not exist. Architectures benchmarked only ImageNet-pretrained (AlexNet, VGG16, DenseNet-121, EfficientNet-B4 in MedMNIST+) are deliberately absent: this project dropped ImageNet weights over redistribution terms, so their published numbers are not comparable to a from-scratch run.
Honest scope
This model is published as part of a research proof of concept, not as a production system.
Numbers above are what this repository measured on the split described, with the code in
scripts/train/. Where a figure is carried from the literature rather than measured here,
it is labelled as such.