Deep learning based automatic detection of uninformative images in pulmonary optical endomicroscopy

Pulmonary optical endomicroscopy (POE) is a real-time imaging technology. It allows to examine pulmonary alveoli at a microscopic level. Acquired in clinical settings, a POE image sequence can have a proportion of more than 25% of the sequence being uninformative frames (i.e., pure-noise and motion artefacts). For a future data analysis, these uninformative frames must be first removed from the sequence. Therefore, the objective of our work is to develop an automatic detection method of uninformative images in endomicroscopy images. We propose to take the detection problem as a classification one. Considering advantages of deep learning methods, a classifier based on CNN (Convolutional Neural Network) is designed with a new loss function based on Havrda-Charvat entropy. It is a generalized Shannon entropy which is a classical loss function. We propose to use this formula to get a better hold on all sorts of data since it provides a model more stable than the Shannon entropy. Our method is tested on a POE dataset including 2947 distinct images, and showing better results than using Shannon entropy.