Abstract
Accurate segmentation of grapevines in imagery acquired from unmanned aerial vehicles (UAVs) is important for precision viticulture, as it supports vineyard management by monitoring grapevine health, growth, and environmental stress. However, the structural diversity of vineyards, including differences in training systems, row curvatures, and foliage density, presents challenges for grapevine segmentation methods. This study evaluates the performance of deep learning (DL) models-Feature Pyramid Network (FPN), Pyramid Scene Parsing Network (PSPNet) and U-Net-each combined with different backbones for grapevine segmentation in UAV-based RGB orthophoto mosaics. Data were collected under a range of vineyard conditions and scenarios from Portugal's Douro and Vinhos Verdes regions, providing a representative dataset across multiple vineyard configurations. The DL models were trained, tested, and evaluated using orthorectified RGB imagery, and their segmentation accuracy was compared to thresholding techniques. The results show that DL models, particularly U-Net, achieved accurate grapevine segmentation and reduced over-segmentation and false detections that are common in thresholding methods. FPN models with Inception-v4 and Xception backbones performed well in vineyards with inter-row vegetation, while PSPNet models showed segmentation limitations. Overall, DL-based segmentation models demonstrated advantages over thresholding approaches, demonstrating their suitability for UAV-based grapevine segmentation in diverse and challenging vineyard environments. These results support the scalability of DL-based segmentation for vineyard monitoring applications and indicate that improved segmentation accuracy can contribute to decision support in precision viticulture.

Abstract

Abstract
This article presents an investigation into the classification of endoscopic capsule pathologies using Multiple Instance Learning (MIL) methods in conjunction with deep neural network architectures. The primary problem addressed in this study is the accurate and efficient detection of gastrointestinal pathologies, a significant challenge in medical diagnostics that can have a profound impact on patient outcomes. The use of endoscopic capsules is particularly important as they provide a minimally invasive method to capture comprehensive images of the gastrointestinal tract, facilitating early detection of conditions such as ulcers, polyps, bleeding, and Crohn's disease. Specifically, we explore three variants of MIL-Max, Mean, and Attention-for analysing sets of images captured by the endoscopic capsule. MIL was employed because it effectively handles scenarios where individual image instances are not explicitly labelled but are grouped in bags with known labels, making it suitable for the complex nature of endoscopic data. Furthermore, MIL has not yet been extensively applied in this modality, highlighting the innovative aspect of our approach. In addition, we evaluated the performance of three convolutional neural network architectures-VGG16, ResNet50, and DenseNet121-in the classification task. The results indicate that the combination of MIL methods and deep neural network architectures offers a promising approach to the detection and classification of gastrointestinal pathologies, with significant improvements in diagnostic accuracy and efficiency. © 2025 The Author(s).

Abstract
Capsule endoscopy is a medical technique for gastrointestinal examinations that is much more advantageous than traditional endoscopy. Medical specialists use RapidReaderTM to annotate endoscopic capsule video images (VCE). This process is time-consuming, error-prone, and expensive. The videos do not retain temporal markers, making it challenging to locate the annotated frames directly. Moreover, the annotated images often undergo enhancement and artifacts creation, which changes their resolution and visual properties compared to the original frames. This study proposes an approach to aid annotation using Deep Learning and content-based image Retrieval (CBIR) techniques to address this issue. A Siamese network with ResNet-18 architecture was trained to compare two medical images through their features and, with a classifier, assess whether they are a match or a mismatch. This methodology was evaluated on a dataset totalling 5792 image pairs and was subjected to several performance metrics: loss, accuracy, AUC (Area Under the Curve), precision, and recall. Various learning rates and optimizers were tested: Adam, SGD, and Adadelta highlighted the Adam optimizer with the best results. This approach produced an accuracy of 97.6% and an AUC of 0.9764 using the Adam optimizer, highlighting the model's potential to reduce manual annotation time significantly. © 2025 The Author(s).

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.