Abstract
Cancer diagnosis is of major importance in the field of human medical pathology, wherein a cell division process known as mitosis constitutes a relevant biological pattern analyzed by professional experts, who seek for such occurrence in presence and number through visual observation of microscopic imagery. This is a time-consuming and exhausting task that can benefit from modern artificial intelligence approaches, namely those handling object detection through deep learning, from which YOLO can be highlighted as one of the most successful, and, as such, a good candidate for performing automatic mitoses detection. Considering that low sensibility for rotation/flip variations is of high importance to ensure mitosis deep detection robustness, in this work, we propose an offline augmentation procedure focusing rotation operations, to address the impact of lost/clipped mitoses induced by online augmentation. YOLOv4 and YOLOv5 were compared, using an augmented test dataset with an exhaustive set of rotation angles, to investigate their performance. YOLOv5 with a mixture of offline and online rotation augmentation methods presented the best averaged F1-score results over three runs.
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Acknowledgements
This work was financed by the project “iPATH, An Intelligent Network Center for Digital Pathology” (N° POCI-01-0247-FEDER-047069 and CENTRO-01-0247-FEDER-047069), financed by Portugal 2020, under the Competitiveness and Internationalization Operational Program, the Lisbon Regional Operational Program, and by the European Regional Development Fund (ERDF).
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Gonzalez, D.G. et al. (2023). Evaluating Rotation Invariant Strategies for Mitosis Detection Through YOLO Algorithms. In: Cunha, A., M. Garcia, N., Marx Gómez, J., Pereira, S. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 484. Springer, Cham. https://doi.org/10.1007/978-3-031-32029-3_3
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