Abstract
The paper addresses the medical challenge of interpreting histopathological slides through expert-independent automated learning with implicit feature determination and direct grading establishment. Deep convolutional neural networks model the image collection and are able to give a timely and accurate support for pathologists, who are more than often burdened by large amounts of data to be processed. The paradigm is however known to be problem-dependent in variable setting, therefore automatic parametrization is also considered. Due to the large necessary runtime, this is restricted to kernel size optimization in each convolutional layer. As processing time still remains considerable for five variables, a surrogate model is further constructed. Results support the use of the deep learning methodology for computational assistance in cancer grading from histopathological images.
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Notes
- 1.
IMEDIATREAT Project, https://sites.google.com/site/imediatreat/.
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Acknowledgments
The second and third authors acknowledge the support of the research grant no. 26/2014, IMEDIATREAT - Intelligent Medical Information System for the Diagnosis and Monitoring of the Treatment of Patients with Colorectal Neoplasm -of the Romanian Ministry of National Education - Research and the Executive Agency for Higher Education Research Development and Innovation Funding.
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Postavaru, S., Stoean, R., Stoean, C., Caparros, G.J. (2017). Adaptation of Deep Convolutional Neural Networks for Cancer Grading from Histopathological Images. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10306. Springer, Cham. https://doi.org/10.1007/978-3-319-59147-6_4
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