Abstract
Biometric recognition and presentation attack detection (PAD) methods strongly rely on deep learning algorithms. Though often more accurate, these models operate as complex black boxes. Interpretability tools are now being used to delve deeper into the operation of these methods, which is why this work advocates their integration in the PAD scenario. Building upon previous work, a face PAD model based on convolutional neural networks was implemented and evaluated both through traditional PAD metrics and with interpretability tools. An evaluation on the stability of the explanations obtained from testing models with attacks known and unknown in the learning step is made. To overcome the limitations of direct comparison, a suitable representation of the explanations is constructed to quantify how much two explanations differ from each other. From the point of view of interpretability, the results obtained in intra and inter class comparisons led to the conclusion that the presence of more attacks during training has a positive effect in the generalisation and robustness of the models. This is an exploratory study that confirms the urge to establish new approaches in biometrics that incorporate interpretability tools. Moreover, there is a need for methodologies to assess and compare the quality of explanations.

Abstract

Abstract
Multiple Sclerosis (MS) is a chronic and inflammatory disorder that causes degeneration of axons in brain white matter and spinal cord. Magnetic Resonance Imaging (MRI) is extensively used to identify MS lesions and evaluate the progression of the disease, but the manual identification and quantification of lesions are time consuming and error-prone tasks. Thus, automated Deep Learning methods, in special Convolutional Neural Networks (CNNs), are becoming popular to segment medical images. It has been noticed that the performance of those methods tends to decrease when applied to MRI acquired under different protocols. The aim of this work is to statistically evaluate the possible influence of domain adaptation during the training process of CNNs models for segmenting MS lesions in MRI. The segmentation models were tested on MRIs (FLAIR and T1) of 20 patients diagnosed with Multiple Sclerosis. The set of segmented images of each different model was compared statistically, through the metrics Dice Similarity Coefficient (DSC), Predictive Positive Value (PPV) and Absolute Volume Difference (AVD). The results indicate that the domain adapted training can improve the performance of automatic segmentation methods, by CNNs, and have great potential to be used in medical clinics in the future. © 2021 IEEE.

Abstract

Abstract

Abstract
Dermoscopic images allow the detailed examination of subsurface characteristics of the skin, which led to creating several substantial databases of diverse skin lesions. However, the dermoscope is not an easily accessible tool in some regions. A less expensive alternative could be acquiring medium resolution clinical macroscopic images of skin lesions. However, the limited volume of macroscopic images available, especially mobile-acquired, hinders developing a clinical mobile-based deep learning approach. In this work, we present a technique to efficiently utilize the sizable number of dermoscopic images to improve the segmentation capacity of macroscopic skin lesion images. A Cycle-Consistent Adversarial Network is used to translate the image between the two distinct domains created by the different image acquisition devices. A visual inspection was performed on several databases for qualitative evaluation of the results, based on the disappearance and appearance of intrinsic dermoscopic and macroscopic features. Moreover, the Frechet Inception Distance was used as a quantitative metric. The quantitative segmentation results are demonstrated on the available macroscopic segmentation databases, SMARTSKINS and Dermofit Image Library, yielding test set thresholded Jaccard Index of 85.13% and 74.30%. These results establish a new state-of-the-art performance in the SMARTSKINS database.