Abstract
Deep learning classifiers applied to structural MRI achieve high performance detecting Alzheimer’s disease, yet their failure modes are under-studied. We identify a subgroup persistently misclassified across 100 model instances with a markedly different atrophy subtype distribution, and show that reclassification from false-negative to true-positive over longitudinal follow-up can take up to five years.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/MLCN_2026_045.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: https://openreview.net/forum?id=2e9BECpalR
BibTex
@InProceedings{StaDid_What_MICCAISAT2026,
author = { Stark, Didem AND Shin, Hwajin AND Münster, Nicolas AND Federmann, Lydia AND Ritter, Kerstin},
title = { { What Do Persistent Misclassifications Tell Us About Alzheimer’s Disease Detection using Structural MRI? } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17255},
month = {pending},
page = {pending}
}