Abstract
Detecting polyps in endoscopic images is essential in healthcare, requiring Explainable Artificial Intelligence (XAI) techniques to ensure transparency and confidence in AI models. Example-based XAI approaches, such as Anchors and Integrated Gradients, are promising but still need to be explored to improve the interpretability of models. In this study, a model was developed that achieved 91% accuracy on the test set. Anchors provided clear and intuitive explanations by highlighting critical regions, such as the polyp area, making it easier for clinical experts to understand the model's decisions. Integrated Gradients offered a detailed pixel-by-pixel analysis, covering the polyp area and other parts of the image, providing a comprehensive view of the model's behaviour. The comparative analysis revealed that Anchors are particularly useful for clarity, while Integrated Gradients offer greater depth and granularity. The combined use of these techniques improves the interpretability of AI models, increasing confidence and acceptance in critical healthcare applications and supporting informed clinical decisions. © 2025 The Author(s).

Abstract
The recent emergence of whole slide images has boosted the use of computer vision techniques and artificial intelligence in digital pathology. Mitosis counting is one of the processes that has benefited from these advances. Also, active learning, an iterative machine learning technique, has emerged as a promising approach to address the challenges associated with mitosis counting problems. One of them is the reduction of the workload of medical specialists in the annotation of datasets used to train deep learning models. This article presents a comprehensive review of the application of active learning for mitosis counting, highlighting its potential to improve detection accuracy and reduce annotation efforts. © 2025 The Authors.

Abstract
This study reviews deep learning techniques and high-resolution satellite images to analyse urban morphology changes in developing countries. The goal is to create a system that can automatically identify and monitor changes in urban areas, such as buildings, roads, and green spaces, to provide accurate data for urban analysis and planning. The project aims to achieve detailed segmentation of urban objects in satellite images by utilising advanced convolutional neural network architectures and efficient image processing methodologies. The results from this study are expected to enhance urban planning and management, addressing the challenges faced by rapidly growing urban centres in developing countries. © 2025 The Author(s).

Abstract
Building rehabilitation is a reality; all rehabilitation work phases must be efficient and sustainable and promote healthy living places. Current procedures for assessing construction conditions are time-consuming, labour-intensive, and costly. They can threaten engineers' health and safety, especially when inspecting hard-to-reach and high-altitude sites. At the initial stage, a survey of the condition of the building is conducted, which later implies the preparation of a report on the existing pathologies, intervention solutions and associated costs. This procedure involves an inspection of the site (through photographs and videos). In addition, biological growths can threaten the health of those who frequent these places. The World Health Organization states that the most important effects are the increased prevalence of respiratory symptoms, allergies, asthma, and immune system disorders. This work aims to raise awareness of this fact and contribute to the identification of an automatic form of biological growth-type defects in images of buildings. To make this possible, we need a dataset of imaging building components with and without biological growths. Subsequently, deep learning methods are applied to allow the automatic identification of this type of defect in the images, and the results are analysed. A pre-trained VGG16 model was used. The dataset was annotated and divided into groups for training, validation, and testing. The model achieved an overall accuracy of 90%. This work demonstrates the potential of using Deep Learning (DL) in the maintenance and rehabilitation of urban infrastructures, highlighting the efficiency and sustainability of these processes and the importance of adjustments to ensure the stability of AI models. © 2025 The Author(s).

Abstract
Diabetic retinopathy is an eye disease that affects people with diabetes mellitus, causing lesions that affect the retina, leading to progressive vision loss. In Portugal, it is estimated that 1.5 million people between the ages of 20 and 79 have diabetes, a figure that is expected to rise in the coming years. This increase is also likely to raise the total number of people affected by diabetic retinopathy, who will need to be identified in the early stages of the disease to receive clinical treatment aimed at reducing the likelihood of visual impairment due to the disease. Detection and classification of the stage of severity is carried out by specialists using medical images of the retina and patients' clinical data. Fundus photographs are the standard for detecting and monitoring the progression of the disease, as they make it possible to see biomarkers that characterize the stages of the disease. The manual task of analyzing and tracing images is time-consuming and subjective, which can lead to interpretation errors. Artificial intelligence (AI) models, such as convolutional neural networks, have been proposed to aid specialists in medical image analysis tasks, some of which have already been approved for clinical use. To overcome the limitations of convolutional networks, new AI models have been proposed to develop computer vision applications, achieving promising results in image classification. The vision mamba model was recently introduced, which uses bidirectional state space to obtain an efficient visual representation. In this work, we evaluate the vision mamba model's ability to detect cases in the moderate and advanced stages of diabetic retinopathy in fundus photographs and compare its performance with models based on convolutional networks. As the best result, the model achieved a recall value of 0.95 in the APTOS dataset. © 2025 The Author(s).

Abstract
Digestive disorders can be signs of long-term conditions such as cancer, and as such, they should be treated seriously. Endoscopic exams of the gastrointestinal tract allow for the early detection of these conditions and facilitate effective treatment; these procedures have their effectiveness limited by variations in operator performance, due to human error. Support systems are desired to help specialists detect and diagnose pathologies in this type of exam. This work used a seldom utilized dataset, the ERS dataset, which contains 121,399 labeled images, to evaluate eight models from the EfficientNet family of architectures, as well as three models from the EfficientNetV2 iteration of this architecture, for the task of binary classification of endoscopic images. This work also compared their performance to four other widely used CNN architectures for the same task, along with the baseline results published by the authors of the dataset. Each model was evaluated in a 5-fold cross-validation procedure, following the same training protocol. The experiments have shown that the best-performing architecture was EfficientNetV2M, followed closely by EfficientNetB7, with the former achieving average accuracy and F1-Score values of, respectively, 82.24% and 88.15%. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.