Abstract
Cervical cancer remains a significant cause of mortality in low-income countries. As in many other diseases, the existence of several screening/diagnosis methods and subjective physician preferences creates a complex ecosystem for automated methods. In order to diminish the amount of labeled data from each modality/expert we propose a regularization-based transfer learning strategy that encourages source and target models to share the same coefficient signs. We instantiated the proposed framework to predict cross-modality individual risk and cross-expert subjective quality assessment of colposcopic images for different modalities. Thus, we are able to transfer knowledge gained from one expert/modality to another.
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Acknowledgements
This work was funded by the Project “NanoSTIMA: Macro-to-Nano Human Sensing: Towards Integrated Multimodal Health Monitoring and Analytics/NORTE-01-0145-FEDER-000016” financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF), and also by Fundação para a Ciência e a Tecnologia (FCT) within PhD grant number SFRH/BD/93012/2013. The authors would like to thank the Gynecology Service of the Hospital Universitario de Caracas. In particular, we would like to recognize the efforts of Drs. Geramel Montero, Dulce Almeida, Jose Valentin, Leonardo Amado and Leticia Parpacen.
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Fernandes, K., Cardoso, J.S., Fernandes, J. (2017). Transfer Learning with Partial Observability Applied to Cervical Cancer Screening. In: Alexandre, L., Salvador Sánchez, J., Rodrigues, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2017. Lecture Notes in Computer Science(), vol 10255. Springer, Cham. https://doi.org/10.1007/978-3-319-58838-4_27
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DOI: https://doi.org/10.1007/978-3-319-58838-4_27
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