Abstract
In this article, we address the problem of the classification of the health state of the colon’s wall of mice, possibly injured by cancer with machine learning approaches. This problem is essential for translational research on cancer and is a priori challenging since the amount of data is usually limited in all preclinical studies for practical and ethical reasons. Three states considered including cancer, health, and inflammatory on tissues. Fully automated machine learning-based methods are proposed, including deep learning, transfer learning, and shallow learning with SVM. These methods addressed different training strategies corresponding to clinical questions such as the automatic clinical state prediction on unseen data using a pre-trained model, or in an alternative setting, real-time estimation of the clinical state of individual tissue samples during the examination. Experimental results show the best performance of 99.93% correct recognition rate obtained for the second strategy as well as the performance of 98.49% which were achieved for the more difficult first case.
Highlights
The characterization of colon’s pathology is realized from histology[1] but is investigated with in vivo imaging techniques which enable the oncological[2] early detection of abnormal physiological processes such as inflammation of dysplastic lesions
We go beyond the sole characterization and, for the first time on Mice colon in cancer study from confocal laser endomicroscopy, in the growing trend of machine learning applied to medical image analysis[11,12,13], propose a fully automated classification method based on supervised learning that we validate on thousands of images
Another a priori open question addressed in the preclinical study is the question of translational research, i.e. the reusability of the knowledge gained for animals on human or human on animals
Summary
The characterization of colon’s pathology is realized from histology[1] but is investigated with in vivo imaging techniques which enable the oncological[2] early detection of abnormal physiological processes such as inflammation of dysplastic lesions. We go beyond the sole characterization (feature handcrafting) and, for the first time on Mice colon in cancer study from confocal laser endomicroscopy, in the growing trend of machine learning applied to medical image analysis[11,12,13], propose a fully automated classification method based on supervised learning that we validate on thousands of images. We discuss the performance obtained with different machine learning approaches when we learn on images corresponding to a given set of mice while applying the classification on a distinct cohort of mice This cross-subject training is relevant for clinical purposes because it quantifies to which extend the disease observed is generic or patient-specific. This body of work based on the classical methodology of handcrafted feature design (taking into account domain knowledge), followed by supervised machine learning
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