Abstract
Deep Learning achieves state-of-the-art results in many domains, yet its black-box nature limits its application to real-world contexts. An intuitive way to improve the interpretability of Deep Learning models is by explaining their decisions with similar cases. However, case-based explanations cannot be used in contexts where the data exposes personal identity, as they may compromise the privacy of individuals. In this work, we identify the main limitations and challenges in the anonymization of case-based explanations of image data through a survey on case-based interpretability and image anonymization methods. We empirically analyze the anonymization methods in regards to their capacity to remove personally identifiable information while preserving relevant semantic properties of the data. Through this analysis, we conclude that most privacy-preserving methods are not sufficiently good to be applied to case-based explanations. To promote research on this topic, we formalize the privacy protection of visual case-based explanations as a multi-objective problem to preserve privacy, intelligibility, and relevant explanatory evidence regarding a predictive task. We empirically verify the potential of interpretability saliency maps as qualitative evaluation tools for anonymization. Finally, we identify and propose new lines of research to guide future work in the generation of privacy-preserving case-based explanations.

Abstract
The growing importance of the Explainable Artificial Intelligence (XAI) field has led to the proposal of several methods for producing visual heatmaps of the classification decisions of deep learning models. However, visual explanations are not sufficient because different end-users have different backgrounds and preferences. Natural language explanations (NLEs) are inherently understandable by humans and, thus, can complement visual explanations. Therefore, we introduce a novel architecture based on multimodal Transformers to enable the generation of NLEs for image classification tasks. Contrary to the current literature, which models NLE generation as a supervised image captioning problem, we propose to learn to generate these textual explanations without their direct supervision, by starting from image captions and evolving to classification-relevant text. Preliminary experiments on a novel dataset where there is a clear demarcation between captions and NLEs show the potential of the approach and shed light on how it can be improved.

Abstract
Simple Summary Nowadays, colorectal cancer is the third most incident cancer worldwide and, although it can be detected by imaging techniques, diagnosis is always based on biopsy samples. This assessment includes neoplasia grading, a subjective yet important task for pathologists. With the growing availability of digital slides, the development of robust and high-performance computer vision algorithms can help to tackle such a task. In this work, we propose an approach to automatically detect and grade lesions in colorectal biopsies with high sensitivity. The presented model attempts to support slide decision reasoning in terms of the spatial distribution of lesions, focusing the pathologist's attention on key areas. Thus, it can be integrated into clinical practice as a second opinion or as a flag for details that may have been missed at first glance. Colorectal cancer (CRC) diagnosis is based on samples obtained from biopsies, assessed in pathology laboratories. Due to population growth and ageing, as well as better screening programs, the CRC incidence rate has been increasing, leading to a higher workload for pathologists. In this sense, the application of AI for automatic CRC diagnosis, particularly on whole-slide images (WSI), is of utmost relevance, in order to assist professionals in case triage and case review. In this work, we propose an interpretable semi-supervised approach to detect lesions in colorectal biopsies with high sensitivity, based on multiple-instance learning and feature aggregation methods. The model was developed on an extended version of the recent, publicly available CRC dataset (the CRC+ dataset with 4433 WSI), using 3424 slides for training and 1009 slides for evaluation. The proposed method attained 90.19% classification ACC, 98.8% sensitivity, 85.7% specificity, and a quadratic weighted kappa of 0.888 at slide-based evaluation. Its generalisation capabilities are also studied on two publicly available external datasets.

Abstract
The field of Deep Learning (DL) has seen a remarkable series of developments with increasingly accurate and robust algorithms. However, the increase in performance has been accompanied by an increase in the parameters, complexity, and training and inference time of the models, which means that we are rapidly reaching a point where DL may no longer be feasible. On the other hand, some specific applications need to be carefully considered when developing DL models due to hardware limitations or power requirements. In this context, there is a growing interest in efficient DL algorithms, with Spiking Neural Networks (SNNs) being one of the most promising paradigms. Due to the inherent asynchrony and sparseness of spike trains, these types of networks have the potential to reduce power consumption while maintaining relatively good performance. This is attractive for efficient DL and, if successful, could replace traditional Artificial Neural Networks (ANNs) in many applications. However, despite significant progress, the performance of SNNs on benchmark datasets is often lower than that of traditional ANNs. Moreover, due to the non-differentiable nature of their activation functions, it is difficult to train SNNs with direct backpropagation, so appropriate training strategies must be found. Nevertheless, significant efforts have been made to develop competitive models. This survey covers the main ideas behind SNNs and reviews recent trends in learning rules and network architectures, with a particular focus on biologically inspired strategies. It also provides some practical considerations of state-of-the-art SNNs and discusses relevant research opportunities.

Abstract
Over the years, the evolution of face recognition (FR) algorithms has been steep and accelerated by a myriad of factors. Motivated by the unexpected elements found in real-world scenarios, researchers have investigated and developed a number of methods for occluded face recognition (OFR). However, due to the SarS-Cov2 pandemic, masked face recognition (MFR) research branched from OFR and became a hot and urgent research challenge. Due to time and data constraints, these models followed different and novel approaches to handle lower face occlusions, i.e., face masks. Hence, this study aims to evaluate the different approaches followed for both MFR and OFR, find linked details about the two conceptually similar research directions and understand future directions for both topics. For this analysis, several occluded and face recognition algorithms from the literature are studied. First, they are evaluated in the task that they were trained on, but also on the other. These methods were picked accordingly to the novelty of their approach, proven state-of-the-art results, and publicly available source code. We present quantitative results on 4 occluded and 5 masked FR datasets, and a qualitative analysis of several MFR and OFR models on the Occ-LFW dataset. The analysis presented, sustain the interoperable deployability of MFR methods on OFR datasets, when the occlusions are of a reasonable size. Thus, solutions proposed for MFR can be effectively deployed for general OFR.

Abstract
Manual assessment of fragments during the pro-cessing of pathology specimens is critical to ensure that the material available for slide analysis matches that captured during grossing without losing valuable material during this process. However, this step is still performed manually, resulting in lost time and delays in making the complete case available for evaluation by the pathologist. To overcome this limitation, we developed an autonomous system that can detect and count the number of fragments contained on each slide. We applied and compared two different methods: conventional machine learning methods and deep convolutional network methods. For conventional machine learning methods, we tested a two-stage approach with a supervised classifier followed by unsupervised hierarchical clustering. In addition, Fast R-CNN and YOLOv5, two state-of-the-art deep learning models for detection, were used and compared. All experiments were performed on a dataset comprising 1276 images of colorec-tal biopsy and polypectomy specimens manually labeled for fragment/set detection. The best results were obtained with the YOLOv5 architecture with a map@0.5 of 0.977 for fragment/set detection.