Refactoring and performance analysis of the main CNN architectures: using false negative rate minimization to solve the clinical images melanoma detection problem.

BACKGROUND: Melanoma is one of the deadliest tumors in the world. Early detection is critical for first-line therapy in this tumor pathology and it remains challenging due to the need for histological analysis to ensure correctness in diagnosis. Therefore, multiple computer-aided diagnosis (CAD) systems working on melanoma images were proposed to mitigate the need of a biopsy. However, although the high global accuracy is declared in literature results, the CAD systems for the health fields must focus on the lowest false negative rate (FNR) possible to qualify as a diagnosis support system. The final goal must be to avoid classification type 2 errors to prevent life-threatening situations. Another goal could be to create an easy-to-use system for both physicians and patients. RESULTS: To achieve the minimization of type 2 error, we performed a wide exploratory analysis of the principal convolutional neural network (CNN) architectures published for the multiple image classification problem; we adapted these networks to the melanoma clinical image binary classification problem (MCIBCP). We collected and analyzed performance data to identify the best CNN architecture, in terms of FNR, usable for solving the MCIBCP problem. Then, to provide a starting point for an easy-to-use CAD system, we used a clinical image dataset (MED-NODE) because clinical images are easier to access: they can be taken by a smartphone or other hand-size devices. Despite the lower resolution than dermoscopic images, the results in the literature would suggest that it would be possible to achieve high classification performance by using clinical images. In this work, we used MED-NODE, which consists of 170 clinical images (70 images of melanoma and 100 images of naevi). We optimized the following CNNs for the MCIBCP problem: Alexnet, DenseNet, GoogleNet Inception V3, GoogleNet, MobileNet, ShuffleNet, SqueezeNet, and VGG16. CONCLUSIONS: The results suggest that a CNN built on the VGG or AlexNet structure can ensure the lowest FNR (0.07) and (0.13), respectively. In both cases, discrete global performance is ensured: 73% (accuracy), 82% (sensitivity) and 59% (specificity) for VGG; 89% (accuracy), 87% (sensitivity) and 90% (specificity) for AlexNet.

Facial expression analysis in a wild sporting environment.

The scientific community and mass media have already reported the use of nonverbal behavior analysis in sports for athletes' performance. Their conclusions stated that certain emotional expressions are linked to athlete's performance, or even that psychological strategies serve to improve endurance performance. This paper examines the portrayal of well-known emotions and their relationship to the participants of an ultra-distance race in a high-stake environment. For this purpose, we analyzed almost 600 runners captured when they passed through a set of locations placed along the race track. We have observed a correlation between the runners' facial expressions and their performance along the track. Moreover, we have analyzed Action Unit activations and aligned our findings with the state-of-the-art psychological baseline.

Deep learning approach for automatic microplastics counting and classification.

The quantification of microplastics is a needed task to monitor its evolution and model its behavior. However, it is a time demanding task traditionally performed using expensive equipment. In this paper, an architecture based on deep learning networks is presented with the aim of automatically count and classify microplastic particles in the range of 1-5 mm from pictures taken with a digital camera or a mobile phone with a resolution of 16 million pixels or higher. The proposed architecture comprises a first stage, implemented with the U-Net neural network, in charge of making the segmentation of the particles in the image. After the different particles have been isolated, a second stage based on the VGG16 neural network classifies them into three types: fragments, pellets and lines. These three types have been selected for being the most common in the range size under consideration. The experimental evaluation was carried out using images taken with two digital cameras and one mobile phone. The particles used in experiments correspond to samples collected on the beach of Playa del Poris in Tenerife Island, Spain, (28° 09' 51″ N, 16° 25' 54″ W) in August 2018. A Jaccard index value of 0.8 is achieved in the experiments of particles segmentation and an accuracy of 98.11% is obtained in the classification of the microplastic particles. The proposed architecture is remarkable faster than a similar previously published system based on traditional computer vision techniques.

Personal Guides: Heterogeneous Robots Sharing Personal Tours in Multi-Floor Environments.

GidaBot is an application designed to setup and run a heterogeneous team of robots to act as tour guides in multi-floor buildings. Although the tours can go through several floors, the robots can only service a single floor, and thus, a guiding task may require collaboration among several robots. The designed system makes use of a robust inter-robot communication strategy to share goals and paths during the guiding tasks. Such tours work as personal services carried out by one or more robots. In this paper, a face re-identification/verification module based on state-of-the-art techniques is developed, evaluated offline, and integrated into GidaBot's real daily activities, to avoid new visitors interfering with those attended. It is a complex problem because, as users are casual visitors, no long-term information is stored, and consequently, faces are unknown in the training step. Initially, re-identification and verification are evaluated offline considering different face detectors and computing distances in a face embedding representation. To fulfil the goal online, several face detectors are fused in parallel to avoid face alignment bias produced by face detectors under certain circumstances, and the decision is made based on a minimum distance criterion. This fused approach outperforms any individual method and highly improves the real system's reliability, as the tests carried out using real robots at the Faculty of Informatics in San Sebastian show.

On the use of simple geometric descriptors provided by RGB-D sensors for re-identification.

The re-identification problem has been commonly accomplished using appearance features based on salient points and color information. In this paper, we focus on the possibilities that simple geometric features obtained from depth images captured with RGB-D cameras may offer for the task, particularly working under severe illumination conditions. The results achieved for different sets of simple geometric features extracted in a top-view setup seem to provide useful descriptors for the re-identification task, which can be integrated in an ambient intelligent environment as part of a sensor network.