Abstract
Gastric Cancer (GC) and Colorectal Cancer (CRC) are some of the most common cancers in the world. The most common diagnostic methods are upper endoscopy and biopsy. Possible expert distractions can lead to late diagnosis. GC is a less studied malignancy than CRC, leading to scarce public data that difficult the use of AI detection methods, unlike CRC where public data are available. Considering that CRC endoscopic images present some similarities with GC, a CRC Transfer Learning approach could be used to improve AI GC detectors. This paper evaluates a novel Transfer Learning approach for real-time GC detection, using a YOLOv4 model pre-trained on CRC detection. The results achieved are promising since GC detection improved relatively to the traditional Transfer Learning strategy.

Abstract
Object detection (OD) coupled with active learning (AL) has emerged as a powerful synergy in the field of computer vision, harnessing the capabilities of machine learning (ML) to automatically identify and perform image-based objects localisation while actively engaging human expertise to iteratively enhance model performance and foster machine-based knowledge expansion. Their prior success, demonstrated in a wide range of fields (e.g., industry and medicine), motivated this work, in which a comprehensive and systematic review of OD and AL techniques was carried out, considering reputed technical/scientific publication databases-such as ScienceDirect, IEEE, PubMed, and arXiv-and a temporal range between 2010 and December 2022. The primary inclusion criterion for papers in this review was the application of AL techniques for OD tasks, regardless of the field of application. A total of 852 articles were analysed, and 60 articles were included after full screening. Among the remaining ones, relevant topics such as AL sampling strategies used for OD tasks and groups categorisation can be found, along with details regarding the deep neural network architectures employed, application domains, and approaches used to blend learning techniques with those sampling strategies. Furthermore, an analysis of the geographical distribution of OD researchers across the globe and their affiliated organisations was conducted, providing a comprehensive overview of the research landscape in this field. Finally, promising research opportunities to enhance the AL process were identified, including the development of novel sampling strategies and their integration with different learning techniques.

Abstract
Pathologies in concrete structures, such as cracks, splintering, efflorescence, corrosion spots, and exposed steel bars, can be visually evidenced on the concrete surface. This paper proposes a method for automatically detecting these pathologies from images of the concrete structure. The proposed method uses deep neural networks to detect pathologies in these images. This method results in time savings and error reduction. The paper presents results in detecting the pathologies from wide-angle images containing the overall structure and also for the specific pathology identification task for cropped images of the region of the pathology. Identifying pathologies in cropped images, the classification task could be performed with 99.4% accuracy using cross-validation and classifying cracks. Wide images containing no, one, or several pathologies in the same image, the case of pathology detection, could be analyzed with the YOLO network to identify five pathology classes. The results for detection with YOLO were measured with mAP, mean Average Precision, for five classes of concrete pathology, reaching 11.80% for fissure, 19.22% for fragmentation, 5.62% for efflorescence, 27.24% for exposed bar, and 24.44% for corrosion. Pathology identification in concrete photos can be optimized using deep learning.

Abstract
Automatic Lip-Reading (ALR), also known as Visual Speech Recognition (VSR), is the technological process to extract and recognize speech content, based solely on the visual recognition of the speaker’s lip movements. Besides hearing-impaired people, regular hearing people also resort to visual cues for word disambiguation, every time one is in a noisy environment. Due to the increasingly interest in developing ALR systems, a considerable number of research articles are being published. This article selects, analyses, and summarizes the main papers from 2018 to early 2022, from traditional methods with handcrafted feature extraction algorithms to end-to-end deep learning based ALR which fully take advantage of learning the best features, and of the evergrowing publicly available databases. By providing a recent state-of-the-art overview, identifying trends, and presenting a conclusion on what is to be expected in future work, this article becomes an efficient way to update on the most relevant ALR techniques. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Abstract
Cancer diagnosis is of major importance in the field of human medical pathology, wherein a cell division process known as mitosis constitutes a relevant biological pattern analyzed by professional experts, who seek for such occurrence in presence and number through visual observation of microscopic imagery. This is a time-consuming and exhausting task that can benefit from modern artificial intelligence approaches, namely those handling object detection through deep learning, from which YOLO can be highlighted as one of the most successful, and, as such, a good candidate for performing automatic mitoses detection. Considering that low sensibility for rotation/flip variations is of high importance to ensure mitosis deep detection robustness, in this work, we propose an offline augmentation procedure focusing rotation operations, to address the impact of lost/clipped mitoses induced by online augmentation. YOLOv4 and YOLOv5 were compared, using an augmented test dataset with an exhaustive set of rotation angles, to investigate their performance. YOLOv5 with a mixture of offline and online rotation augmentation methods presented the best averaged F1-score results over three runs. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Abstract
Glaucoma is a disease that arises from increased intraocular pressure and leads to irreversible partial or total loss of vision. Due to the lack of symptoms, this disease often progresses to more advanced stages, not being detected in the early phase. The screening of glaucoma can be made through visualization of the retina, through retinal images captured by medical equipment or mobile devices with an attached lens to the camera. Deep learning can enhance and increase mass glaucoma screening. In this study, domain transfer learning technique is important to better weight initialization and for understanding features more related to the problem. For this, classic convolutional neural networks, such as ResNet50 will be compared with Vision Transformers, in high and low-resolution images. The high-resolution retinal image will be used to pre-trained the network and use that knowledge for detecting glaucoma in retinal images captured by mobile devices. The ResNet50 model reached the highest values of AUC in the high-resolution dataset, being the more consistent model in all the experiments. However, the Vision Transformer proved to be a promising technique, especially in low-resolution retinal images. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.