Abstract
Cancer ranks as a leading cause of death and an important barrier to increasing life expectancy in every country of the world. For this reason, there is a great requirement for developing computer-aided approaches for accurate cancer diagnosis and grading that can overcome the problem of intra- and inter-observer inconsistency and thereby improve the accuracy and consistency in the cancer detection and treatment planning processes. In particular, the studies exploiting deep learning for automatic grading of colon carcinoma are still in infancy since the works in the literature did not exploit the most advanced models and methodologies of machine learning and systematic exploration of the most promising available convolutional networks is missing. To fill this gap, in this work, the most performing convolutional architectures in classification tasks have been exploited to improve colon carcinoma grading in histological images. The experimental proofs on the largest publicly available dataset demonstrated marked improvement with respect to the leading state of the art approaches.
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Change history
15 May 2022
In the originally published version of chapter 20, the name of the author Pierluigi Carcagnì contained a spelling mistake. This has been corrected.
Notes
- 1.
World Health Organization (WHO). Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000–2019. WHO; 2020. Accessed March 8, 2021.
- 2.
- 3.
Data for previous works were taken from original papers.
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Acknowledgment
The authors would like to thank Mr. Arturo Argentieri from CNR-ISASI Italy for his technical contribution on the multi-GPU computing facilities and in setting up and maintain the publicly available dataset.
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Leo, M., Carcagnì, P., Signore, L., Benincasa, G., Laukkanen, M.O., Distante, C. (2022). Improving Colon Carcinoma Grading by Advanced CNN Models. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, vol 13231. Springer, Cham. https://doi.org/10.1007/978-3-031-06427-2_20
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