Abstract
The early diagnosis of a cancer type is a fundamental goal in cancer treatment, as it can facilitate the subsequent clinical management of patients. The leading importance of classifying cancer patients into high or low risk groups has led many research teams, both from biomedical and bioinformatics field, to study the application of Deep Learning (DL) methods. The ability of DL tools to detect key features from complex datasets is a fundamental achievement in early diagnosis and cell cancer progression. In this paper, we apply DL approach to classification of osteosarcoma cells. Osteosarcoma is the most common bone cancer occurring prevalently in children or young adults. Glass slides of different cell populations were cultured from Mesenchimal Stromal Cells (MSCs) and differentiated in healthy bone cells (osteoblasts) or osteosarcoma cells. Images of such samples are recorded with an optical microscope. DL is then applied to identify and classify single cells. The results show a classification accuracy of 0.97. The next step is the application of our DL approach to tissue in order to improve digital histopathology.
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Acknowledgements
This work is being carried out partially in the framework of the BIO-ICT joint laboratory between the Institute of Biophysics and the Institute of Information Science and Technologies, both of the National Research Council of Italy, in Pisa.
We would like to thank Nvidia Corporation: this work would have required an invaluable time without a Titan X board powered by Pascal won by Signals & Images Laboratory of CNR-ISTI at the 2017 Nvidia GPU Grant.
We also wish to thank Luisa Trombi, Serena Danti, Delfo D’Alessandro, from University of Pisa, for useful support with biological samples.
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D’Acunto, M., Martinelli, M., Moroni, D. (2019). Deep Learning Approach to Human Osteosarcoma Cell Detection and Classification. In: Choroś, K., Kopel, M., Kukla, E., Siemiński, A. (eds) Multimedia and Network Information Systems. MISSI 2018. Advances in Intelligent Systems and Computing, vol 833. Springer, Cham. https://doi.org/10.1007/978-3-319-98678-4_36
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