Quiz Ref ID Full digitalization of the microscopic evaluation of stained tissue sections in histopathology has become feasible in recent years because of advances in slide scanning technology and cost reduction in digital storage. Advantages of digital pathology include remote diagnostics, immediate availability of archival cases, and easier consultations with expert pathologists.¹ Also, the possibility for computer-aided diagnostics may be advantageous.²

Diatoms, a kind of algae microorganisms with several species, are quite useful for water quality determination, one of the hottest topics in applied biology nowadays. At the same time, deep learning and convolutional neural networks (CNN) are becoming an extensively used technique for image classification in a variety of problems. This paper approaches diatom classification with this technique, in order to demonstrate whether it is suitable for solving the classification problem. An extensive dataset was specifically collected (80 types, 100 samples/type) for this study. The dataset covers different illumination conditions and it was computationally augmented to more than 160,000 samples. After that, CNNs were applied over datasets pre-processed with different image processing techniques. An overall accuracy of 99% is obtained for the 80-class problem and different kinds of images (brightfield, normalized). Results were compared to previous presented classification techniques with different number of samples. As far as the authors know, this is the first time that CNNs are applied to diatom classification.

This paper describes a face detection system which goes beyond traditional face detection approaches normally designed for still images. The system described in this paper has been designed taking into account the temporal coherence contained in a video stream in order to build a robust detector. Multiple and real-time detection is achieved by means of cue combination. The resulting system builds a feature based model for each detected face, and searches them using the various model information in the next frame. The experiments have been focused on video streams, where our system can actually exploit the benefits of the temporal coherence integration. The results achieved for video stream processing outperform Rowley-Kanade's and Viola-Jones' solutions providing eye and face data in real-time with a notable correct detection rate, approx. 99.9% faces and 87.5% eye pairs on 26338 images.