Keywords: mitosis detection, domain shift, histopathology
TL;DR: We evaluated the scanner-caused domain shift on the novel MICCAI-MIDOG 2021 multi-scanner mitosis detection data set and found that it's really strong.
Abstract: Automated detection of mitotic figures in histopathology images has seen vast improvements, thanks to modern deep learning-based pipelines. Application of these methods, however, is in practice limited by strong variability of images between labs. This results in a domain shift of the images, which causes a performance drop of the models. Hypothesizing that the scanner device plays a decisive role in this effect, we evaluated the susceptibility of a standard mitosis detection approach to the domain shift introduced by using a different whole slide scanner. Our work is based on the MICCAI-MIDOG challenge 2021 data set, which includes 200 tumor cases of human breast cancer and four scanners. Our work indicates that the domain shift induced not by biochemical variability but purely by the choice of acquisition device is underestimated so far. Models trained on images of the same scanner yielded an average F1 score of 0.683, while models trained on a single other scanner only yielded an average F1 score of 0.325. Training on another multi-domain mitosis dataset led to mean F1 scores of 0.52. We found this not to be reflected by domain-shifts measured as proxy A distance-derived metric.
Paper Type: validation/application paper
Primary Subject Area: Transfer Learning and Domain Adaptation
Secondary Subject Area: Detection and Diagnosis
Paper Status: original work, not submitted yet
Source Code Url: https://github.com/DeepPathology/MIDOG/tree/main/DomainShiftQuantification
Data Set Url: https://zenodo.org/record/4643381
Registration: I acknowledge that publication of this at MIDL and in the proceedings requires at least one of the authors to register and present the work during the conference.
Authorship: I confirm that I am the author of this work and that it has not been submitted to another publication before.