University of Oulu

Isosalo, A., Inkinen, S. I., Turunen, T., Ipatti, P. S., Reponen, J., & Nieminen, M. T. (2023). Independent evaluation of a multi-view multi-task convolutional neural network breast cancer classification model using Finnish mammography screening data. Computers in Biology and Medicine, 161, 107023.

Independent evaluation of a multi-view multi-task convolutional neural network breast cancer classification model using Finnish mammography screening data

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Author: Isosalo, A.1; Inkinen, S.I.1,2; Turunen, T.3;
Organizations: 1Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
2HUS Diagnostic Center, Clinical Physiology and Nuclear Medicine, Helsinki University and Helsinki University Hospital, Helsinki, Finland
3Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
4Medical Research Centre Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: embargoed
Persistent link:
Language: English
Published: Elsevier, 2023
Publish Date: 2024-05-15


Background: Development of deep convolutional neural networks for breast cancer classification has taken significant steps towards clinical adoption. It is though unclear how the models perform for unseen data, and what is required to adapt them to different demographic populations. In this retrospective study, we adopt an openly available pre-trained mammography breast cancer multi-view classification model and evaluate it by utilizing an independent Finnish dataset.

Methods: Transfer learning was used, and the pre-trained model was finetuned with 8,829 examinations from the Finnish dataset (4,321 normal, 362 malignant and 4,146 benign examinations). Holdout dataset with 2,208 examinations from the Finnish dataset (1,082 normal, 70 malignant and 1,056 benign examinations) was used in the evaluation. The performance was also evaluated on a manually annotated malignant suspect subset. Receiver Operating Characteristic (ROC) and Precision–Recall curves were used to performance measures.

Results: The Area Under ROC [95%CI] values for malignancy classification obtained with the finetuned model for the entire holdout set were 0.82 [0.76, 0.87], 0.84 [0.77, 0.89], 0.85 [0.79, 0.90], and 0.83 [0.76, 0.89] for R-MLO, L-MLO, R-CC and L-CC views respectively. Performance on the malignant suspect subset was slightly better. On the auxiliary benign classification task performance remained low.

Conclusions: The results indicate that the model performs well also in an out-of-distribution setting. Finetuning allowed the model to adapt to some of the underlying local demographics. Future research should concentrate to identify breast cancer subgroups adversely affecting performance, as it is a requirement for increasing the model’s readiness level for a clinical setting.

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Series: Computers in biology and medicine
ISSN: 0010-4825
ISSN-E: 1879-0534
ISSN-L: 0010-4825
Issue: In press
DOI: 10.1016/j.compbiomed.2023.107023
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
217 Medical engineering
3122 Cancers
Funding: This study was funded by the Jane and Aatos Erkko Foundation, Helsinki, Finland, and the Technology Industries of Finland Centennial Foundation, Helsinki, Finland. S.I. Inkinen received funding from the Academy of Finland, Helsinki, Finland (project no. 316899). A. Isosalo received funding from the Jenny and Antti Wihuri Foundation, Helsinki, Finland (grant no. 210099).
Academy of Finland Grant Number: 316899
Detailed Information: 316899 (Academy of Finland Funding decision)
Dataset Reference: The datasets generated and analyzed during the current study are not publicly available due to personal data content. The INbreast data that support the findings of this study are available from Breast Research Group, INESC Porto, Portugal, upon reasonable request. The tool used in facilitating the reference annotations for this study is made available at
Copyright information: © 2023. This manuscript version is made available under the CC-BY-NC-ND 4.0 license