Abstract
Lung diseases affect the lives of billions of people worldwide, and 4 million people, each year, die prematurely due to this condition. These pathologies are characterized by specific imagiological findings in CT scans. The traditional Computer-Aided Diagnosis (CAD) approaches have been showing promising results to help clinicians; however, CADs normally consider a small part of the medical image for analysis, excluding possible relevant information for clinical evaluation. Multiple Instance Learning (MIL) approach takes into consideration different small pieces that are relevant for the final classification and creates a comprehensive analysis of pathophysiological changes. This study uses MIL-based approaches to identify the presence of lung pathophysiological findings in CT scans for the characterization of lung disease development. This work was focus on the detection of the following: Fibrosis, Emphysema, Satellite Nodules in Primary Lesion Lobe, Nodules in Contralateral Lung and Ground Glass, being Fibrosis and Emphysema the ones with more outstanding results, reaching an Area Under the Curve (AUC) of 0.89 and 0.72, respectively. Additionally, the MIL-based approach was used for EGFR mutation status prediction - the most relevant oncogene on lung cancer, with an AUC of 0.69. The results showed that this comprehensive approach can be a useful tool for lung pathophysiological characterization.

Abstract
Building rehabilitation is a reality, and all phases of rehabilitation work need to be efficient and sustainable. Current procedures for assessing construction conditions are time-consuming, laborious and expensive and pose threats to the health and safety of engineers, especially when inspecting locations not easy to access. In an initial step, a survey of the condition of the building is carried out, which subsequently implies the elaboration of a report of existing pathologies, intervention solutions and associated costs. This survey involves an inspection of the site (through photographs and videos). This work aims to detect and locate cracks defects in images of painted facade walls of buildings. A VGG16 pre-trained model was evaluated first on a public database with cracked and not cracked concrete surfaces and then on a private database of images of painted building facades with and without cracks. The predicted activation maps were analysed with Grad-CAM methods to validate the models’ prediction. The proposed model achieved 99% accuracy on the concrete public dataset and 78% on the building's facade private dataset. The limitations and the future works are identified. © 2022, University of Cantabria - Building Technology R&D Group. All rights reserved.

Abstract
Diabetes has significant effects on the human body, one of which is the increase in the blood pressure and when not diagnosed early, can cause severe vision complications and even lead to blindness. Early screening is the key to overcoming such issues which can have a significant impact on rural areas and overcrowded regions. Mobile systems can help bring the technology to those in need. Transfer learning based Deep Learning algorithms combined with mobile retinal imaging systems can significantly reduce the screening time and lower the burden on healthcare workers. In this paper, several efficiency factors of Diabetic Retinopathy detection systems based on Convolutional Neural Networks are tested and evaluated for mobile applications. Two main techniques are used to measure the efficiency of DL based DR detection systems. The first method evaluates the effect of dataset change, where the base architecture of the DL model remains the same. The second method measures the effect of base architecture variation, where the dataset remains unchanged. The results suggest that the inclusivity of the datasets, and the dataset size significantly impact the DR detection accuracy and sensitivity. Amongst the five chosen lightweight architectures, EfficientNet-based DR detection algorithms outperformed the other transfer learning models along with APTOS Blindness Detection dataset.

Abstract
Precancerous conditions such as intestinal metaplasia (IM) have a key role in gastric cancer development and can be detected during endoscopy. During upper gastrointestinal endoscopy (UGIE), misdiagnosis can occur due to technical and human factors or by the nature of the lesions, leading to a wrong diagnosis which can result in no surveillance/treatment and impairing the prevention of gastric cancer. Deep learning systems show great potential in detecting precancerous gastric conditions and lesions by using endoscopic images and thus improving and aiding physicians in this task, resulting in higher detection rates and fewer operation errors. This study aims to develop deep learning algorithms capable of detecting IM in UGIE images with a focus on model explainability and interpretability. In this work, white light and narrow-band imaging UGIE images collected in the Portuguese Institute of Oncology of Porto were used to train deep learning models for IM classification. Standard models such as ResNet50, VGG16 and InceptionV3 were compared to more recent algorithms that rely on attention mechanisms, namely the Vision Transformer (ViT), trained in 818 UGIE images (409 normal and 409 IM). All the models were trained using a 5-fold cross-validation technique and for validation, an external dataset will be tested with 100 UGIE images (50 normal and 50 IM). In the end, explainability methods (Grad-CAM and attention rollout) were used for more clear and more interpretable results. The model which performed better was ResNet50 with a sensitivity of 0.75 (±0.05), an accuracy of 0.79 (±0.01), and a specificity of 0.82 (±0.04). This model obtained an AUC of 0.83 (±0.01), where the standard deviation was 0.01, which means that all iterations of the 5-fold cross-validation have a more significant agreement in classifying the samples than the other models. The ViT model showed promising performance, reaching similar results compared to the remaining models.

Abstract
SAR Interferometry (InSAR) techniques are for detecting and monitoring ground deformation all over the planet. Natural disasters such as volcanoes and earthquakes deformations are among the main applications, and the great developments that we have witnessed in recent years suggests that near real-time monitoring will soon be possible. InSAR is developing fast - space agencies are launching more satellites, leading to exponential data growth. Consequently, conventional techniques cannot process all the acquired data. Modern deep learning methods can be a solution since they reach high accuracy in automatically detecting patterns in images and are fast to operate. In this work, we explore the contribution of deep learning vision transformer models to automatically detect seismic deformation in SAR interferograms. A VGG19 model is trained as baseline and ViT model uses 256x256 pixels patches and the full interferogram. The ViT model outperforms the state-of-the-art both for patch and full interferogram approaches, achieving 0.88 and 0.92 F1-score, respectively.

Abstract
Insect pests cause significant damage to agricultural production. Smart pest monitoring enables the automatic detection and identification of pests using artificial intelligence techniques. The automatic detection of pests is an important tool to help the farmer decide on the application of pesticides. Several studies were carried out to develop deep learning methods for detecting insect pests. However, it is still an open problem, as there are a scarcity and data features that do not allow the good performance of a deep learning method. Pest24 is a public dataset with great diversity and variability of insects, but it has a low detection rate. To improve detection performance in Pest24, this work proposes a method of automatic detection of insects using deep learning. Two experiments were carried out, applying the YOLOv5 with standard hyperparameters and the hyperparameter tuning obtained by the evolution algorithm. As a result, we obtained a performance superior to that reported in state of the art, with the YOLOv5 method with standard hyperparameters, with an mAP of 72.1%.