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Segmentation, classification and interpretation of breast cancer medical images using human-in-the-loop machine learning

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Abstract

This paper explores the application of Human-in-the-Loop (HITL) strategies in the training of machine learning models in the medical domain. In this case, a doctor-in-the-loop approach is proposed to leverage human expertise in dealing with large and complex data. Specifically, the paper deals with the use of Whole Slide Imaging (WSI) for the analysis and prediction of the genomic subtype of breast cancer. Three different tasks were developed: segmentation of histopathological images, classification of these images regarding the genomic subtype of the cancer, and finally, interpretation of the machine learning results. The involvement of a pathologist helped us to develop a better segmentation model trying to group areas to make it more useful for further diagnosis. Because the classification models underperformed due to the complexity of the problem and insufficient data for certain cancer types, we focus our efforts in using the feedback from the pathologist to enhance model interpretability through a HITL hyperparameter optimization process.

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Data availibility

The dataset analyzed during the current study is available in the TCGA repository [23], URL: https://portal.gdc.cancer.gov/projects/TCGA-BRCA. The images analyzed during the current study is available in the TCIA repository [25], https://www.cancerimagingarchive.net/collection/tcga-brca/

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Acknowledgements

This work has been supported by the State Research Agency of the Spanish Government (Grants PID2019-107194GB-I00/AEI/10.13039/501100011033 and Project PID2023-147422OB-I00) and by the Xunta de Galicia (Grant ED431C 2022/44), supported by the EU European Regional Development Fund (ERDF). We wish to acknowledge support received from the Centro de Investigación de Galicia CITIC, funded by the Xunta de Galicia and ERDF (Grant ED431C 2022/44). Funding for open access charge: Universidade da Coruña/CISUG. The results published here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.

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Authors and Affiliations

  1. Department of Computer Science and Information Technologies, Universidade da Coruña (CITIC), Campus de Elviña, 15071, A Coruña, Galicia, Spain

    David Vázquez-Lema, Eduardo Mosqueira-Rey, Elena Hernández-Pereira, Carlos Fernandez-Lozano & Fernando Seara-Romera

  2. Servicio de Anatomía Patológica, Complejo Hospitalario Universitario de A Coruña (CHUAC), As Xubias, 84, 15006, A Coruña, Spain

    Jorge Pombo-Otero

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Contributions

David Vázquez-Lema main programmer and developer, also writer, reviewer and editor. Eduardo Mosqueira-Rey main writer of the manuscript, also reviewer and editor. Elena Hernández-Pereira assistant writer in interpretation tasks, also reviewer and editor. Carlos Fernández-Lozano ML specialist in cancer diseases acting as advisor in all the tasks of the paper, also reviewer and editor. Fernando Seara-Romero software engineer in charge of implementing the interpretation task. Jorge Pombo-Otero pathologist in charge of all the tasks involving HITL machine learning.

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Vázquez-Lema, D., Mosqueira-Rey, E., Hernández-Pereira, E. et al. Segmentation, classification and interpretation of breast cancer medical images using human-in-the-loop machine learning. Neural Comput & Applic 37, 3023–3045 (2025). https://doi.org/10.1007/s00521-024-10799-7

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