Abstract
High-quality labels are of utmost importance in biomedicine and instrumental for tasks such as disease understanding, drug discovery, and medical diagnosis. Human-in-the-loop approaches and AI-assisted annotations are currently widely accepted as the safest approach for data curation, offering both speed and human supervision, while leveraging the power of AI. Indeed, foundation models are often used to obtain initial labels which are then manually corrected by a human expert and subsequently used for downstream tasks. In this work, we uncover a rarely discussed risk of such semi-automated approaches and present a case study to demonstrate this. Focusing on microscopy imaging segmentation, where annotation quality is critical and costly, we collected and annotated a Transmission Electron Microscopy dataset in a semiautomated approach, using BATS, a software we built for AI-assisted segmentation labeling in microscopy which encourages data centric practices. We show how batch effects can be derived by a mix of human and model annotations affecting the downstream analysis. Finally, we demonstrate a solution which mitigates these batch effects and discuss how future researchers can adopt responsible and accurate data annotation pipelines.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/HAIC26_001.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=ZJzezbObr1
BibTex
@InProceedings{BukChr_AIassisted_MICCAISAT2026,
author = { Bukas, Christina AND Engler, Jonas AND Pelin, Helena AND Hagenberg, Jonas AND Khan, Haider AND Campi, Francesco AND Eberhagen, Carola AND Zischka, Hans AND Piraud, Marie},
title = { { AI-assisted labeling and its pitfalls: A case study in Electron Microscopy segmentation } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17279},
month = {pending},
page = {pending}
}