Manuel González-Hidalgo

Miquel Miró-Nicolau, Gabriel Moyà-Alcover, Antoni Jaume-i-Capó et al.

The increasing reliance on Deep Learning models, combined with their inherent lack of transparency, has spurred the development of a novel field of study known as eXplainable AI (XAI) methods. These methods seek to enhance the trust of end-users in automated systems by providing insights into the rationale behind their decisions. This paper presents a novel trust measure in XAI systems, allowing their refinement. Our proposed metric combines both performance metrics and trust indicators from an objective perspective. To validate this novel methodology, we conducted three case studies showing an improvement respect the state-of-the-art, with an increased sensitiviy to different scenarios.

Lidia Talavera-Martínez, Pedro Bibiloni, Manuel González-Hidalgo

The process of removing occluding hair has a relevant role in the early and accurate diagnosis of skin cancer. It consists of detecting hairs and restore the texture below them, which is sporadically occluded. In this work, we present a model based on convolutional neural networks for hair removal in dermoscopic images. During the network's training, we use a combined loss function to improve the restoration ability of the proposed model. In order to train the CNN and to quantitatively validate their performance, we simulate the presence of skin hair in hairless images extracted from publicly known datasets such as the PH2, dermquest, dermis, EDRA2002, and the ISIC Data Archive. As far as we know, there is no other hair removal method based on deep learning. Thus, we compare our results with six state-of-the-art algorithms based on traditional computer vision techniques by means of similarity measures that compare the reference hairless image and the one with hair simulated. Finally, a statistical test is used to compare the methods. Both qualitative and quantitative results demonstrate the effectiveness of our network.

Nataša Petrović, Gabriel Moyà-Alcover, Antoni Jaume-i-Capó et al.

In this work we propose an approach to select the classification method and features, based on the state-of-the-art, with best performance for diagnostic support through peripheral blood smear images of red blood cells. In our case we used samples of patients with sickle-cell disease which can be generalized for other study cases. To trust the behavior of the proposed system, we also analyzed the interpretability. We pre-processed and segmented microscopic images, to ensure high feature quality. We applied the methods used in the literature to extract the features from blood cells and the machine learning methods to classify their morphology. Next, we searched for their best parameters from the resulting data in the feature extraction phase. Then, we found the best parameters for every classifier using Randomized and Grid search. For the sake of scientific progress, we published parameters for each classifier, the implemented code library, the confusion matrices with the raw data, and we used the public erythrocytesIDB dataset for validation. We also defined how to select the most important features for classification to decrease the complexity and the training time, and for interpretability purpose in opaque models. Finally, comparing the best performing classification methods with the state-of-the-art, we obtained better results even with interpretable model classifiers.