Abstract

Abstract

Abstract
Convolutional Neural Networks, as well as other deep learning methods, have shown remarkable performance on tasks like classification and detection. However, these models largely remain black-boxes. With the widespread use of such networks in real-world scenarios and with the growing demand of the right to explanation, especially in highly-regulated areas like medicine and criminal justice, generating accurate predictions is no longer enough. Machine learning models have to be explainable, i.e., understandable to humans, which entails being able to present the reasons behind their decisions. While most of the literature focuses on post-model methods, we propose an in-model CNN architecture, composed by an explainer and a classifier. The model is trained end-to-end, with the classifier taking as input not only images from the dataset but also the explainer’s resulting explanation, thus allowing for the classifier to focus on the relevant areas of such explanation. We also developed a synthetic dataset generation framework, that allows for automatic annotation and creation of easy-to-understand images that do not require the knowledge of an expert to be explained. Promising results were obtained, especially when using L1 regularisation, validating the potential of the proposed architecture and further encouraging research to improve the proposed architecture’s explainability and performance. © 2019, Springer Nature Switzerland AG.

Abstract
Services are the backbone of modern economies and are increasingly supported by technology. Meanwhile, there is an accelerated growth of new technologies that are able to learn from themselves, providing more and more relevant results, i.e. Artificial Intelligence (AI). While there have been significant advances on the capabilities of AI, the impacts of this technology on service provision are still unknown. Conceptual research claims that AI offers a way to augment human capabilities or position it as a threat to human jobs. The objective of this study is to better understand the impact of AI on service, namely by understanding current trends in AI, and how they are, and will, impact service provision. To achieve this, a qualitative study, following Grounded Theory methodology was performed, with ten Artificial Intelligence experts selected from industry and academia.

Abstract
Interictal Epileptiform Discharge (IED) detection in EEG signals is widely used in the diagnosis of epilepsy. Visual analysis of EEGs by experts remains the gold standard, outperforming current computer algorithms. Deep learning methods can be an automated way to perform this task. We trained a VGG network using 2-s EEG epochs from patients with focal and generalized epilepsy (39 and 40 patients, respectively, 1977 epochs total) and 53 normal controls (110770 epochs). Five-fold cross-validation was performed on the training set. Model performance was assessed on an independent set (734 IEDs from 20 patients with focal and generalized epilepsy and 23040 normal epochs from 14 controls). Network visualization techniques (filter visualization and occlusion) were applied. The VGG yielded an Area Under the ROC Curve (AUC) of 0.96 (95% Confidence Interval (CI) = 0.95 - 0.97). At 99% specificity, the sensitivity was 79% and only one sample was misclassified per two minutes of analyzed EEG. Filter visualization showed that filters from higher level layers display patches of activity indicative of IED detection. Occlusion showed that the model correctly identified IED shapes. We show that deep neural networks can reliably identify IEDs, which may lead to a fundamental shift in clinical EEG analysis.

Abstract
With the outstanding predictive performance of Convolutional Neural Networks on different tasks and their widespread use in real-world scenarios, it is essential to understand and trust these black-box models. While most of the literature focuses on post-model methods, we propose a novel in-model joint architecture, composed by an explainer and a classifier. This architecture outputs not only a class label, but also a visual explanation of such decision, without the need for additional labelled data to train the explainer besides the image class. The model is trained end-to-end, with the classifier taking as input an image and the explainer's resulting explanation, thus allowing for the classifier to focus on the relevant areas of such explanation. Moreover, this approach can be employed with any classifier, provided that the necessary connections to the explainer are made. We also propose a three-phase training process and two alternative custom loss functions that regularise the produced explanations and encourage desired properties, such as sparsity and spatial contiguity. The architecture was validated in two datasets (a subset of ImageNet and a cervical cancer dataset) and the obtained results show that it is able to produce meaningful image- and class-dependent visual explanations, without direct supervision, aligned with intuitive visual features associated with the data. Quantitative assessment of explanation quality was conducted through iterative perturbation of the input image according to the explanation heatmaps. The impact on classification performance is studied in terms of average function value and AOPC (Area Over the MoRF (Most Relevant First) Curve). For further evaluation, we propose POMPOM (Percentage of Meaningful Pixels Outside the Mask) as another measurable criteria of explanation goodness. These analyses showed that the proposed method outperformed state-of-the-art post-model methods, such as LRP (Layer-wise Relevance Propagation).