Abstract
Cardiovascular diseases are one of the main causes of death in the world. The predominant form of cardiovascular disease is coronary artery disease. Coronary artery calcium scanning is a non-contrast computed tomography exam that is considered the most reliable predictor of coronary events. Deep learning models have been developed for the segmentation of coronary artery calcium but the results have limited interpretability due to the black-box nature of these models. This work proposes an image retrieval pipeline based on a supervised contrastive framework that is capable of enhancing this interpretability by providing similar visual examples of coronary calcifications. In the COCA dataset, it is shown that this retrieval presents a label precision of 0.944 ± 0.230 regarding artery labels of retrieved images, with moderate similarity in terms of calcification area and Agatston score. It is also shown that the retrieval can be used to correct a deep CAC segmentation model by passing predictions from a segmentation model through the retrieval system, improving robustness and explainability.Clinical relevance- This study enhances CAC segmentation through image retrieval, improving both explainability and artery-specific labeling. By providing clinicians with more interpretable and anatomically accurate results, our approach aims to increase confidence in AI-assisted diagnostics leading to better-informed clinical decision-making in coronary artery disease diagnosis.

Abstract
Neuroblastoma (NB) is the most common extracranial tumor in pediatric cases. The MYCN oncogene amplification (MNA) is knowingly correlated with a poor prognosis, making detecting this biomarker crucial for treatment selection and survival prediction. The current clinical protocol for MNA detection includes invasive procedures, such as biopsy. The proposed work aims to develop non-invasive techniques for predicting MNA in patients with diagnosed NB, using AI-based models and Computerized Tomography (CT) scans. Machine learning methods that use the imaging features extracted from the tumor on the CT slices were developed and compared with deep learning (DL) models. Additionally, agnostic explainable methods for imaging were applied to create explanations about the relevant information used by the DL models in the prediction. The results show a better performance for the DL approach, which achieved an AUC of 0.94 ± 0.04. The similarity in the explanations produced by the models trained with different data splits showed that feature extraction remains somewhat invariant to shifts in training data, which is relevant given the small amount of data available. Learning models were shown to have predictive potential that, with further improvements, can be integrated into predictive, explainable, and, thus, trustworthy systems to aid clinicians in the decision-making process. © 2025 IEEE.

Abstract
Objective.The detection of arterial pulsating signals at the skin periphery with Photoplethysmography (PPG) are easily distorted by motion artifacts. This work explores the alternatives to the aid of PPG reconstruction with movement sensors (accelerometer and/or gyroscope) which to date have demonstrated the best pulsating signal reconstruction. Approach. A generative adversarial network with fully connected layers is proposed for the reconstruction of distorted PPG signals. Artificial corruption was performed to the clean selected signals from the BIDMC Heart Rate dataset, processed from the larger MIMIC II waveform database to create the training, validation and testing sets. Main results. The heart rate (HR) of this dataset was further extracted to evaluate the performance of the model obtaining a mean absolute error of 1.31 bpm comparing the HR of the target and reconstructed PPG signals with HR between 70 and 115 bpm. Significance. The model architecture is effective at reconstructing noisy PPG signals regardless the length and amplitude of the corruption introduced. The performance over a range of HR (70-115 bpm), indicates a promising approach for real-time PPG signal reconstruction without the aid of acceleration or angular velocity inputs.

Abstract
Semantic segmentation consists of predicting a semantic label for each image pixel. While existing deep learning approaches achieve high accuracy, they often overlook the ordinal relationships between classes, which can provide critical domain knowledge (e.g., the pupil lies within the iris, and lane markings are part of the road). This paper introduces novel methods for spatial ordinal segmentation that explicitly incorporate these inter-class dependencies. By treating each pixel as part of a structured image space rather than as an independent observation, we propose two regularization terms and a new metric to enforce ordinal consistency between neighboring pixels. Two loss regularization terms and one metric are proposed for structural ordinal segmentation, which penalizes predictions of non-ordinal adjacent classes. Five biomedical datasets and multiple configurations of autonomous driving datasets demonstrate the efficacy of the proposed methods. Our approach achieves improvements in ordinal metrics and enhances generalization, with up to a 15.7% relative increase in the Dice coefficient. Importantly, these benefits come without additional inference time costs. This work highlights the significance of spatial ordinal relationships in semantic segmentation and provides a foundation for further exploration in structured image representations.

Abstract
In nature, and particularly in agriculture, pollination is fundamental for the sustainability of our society. In this context, pollination is a vital process underlying crop yield quality and is responsible for the biodiversity and the standards of the flora. Bees play a crucial role in natural pollination; however, their populations are declining. Robots can help maintain pollination levels while humans work to recover bee populations. Swarm robotics approaches appear promising for robotic pollination. This paper proposes the cooperation between multiple Unmanned Aerial Vehicles (UAVs) and an Unmanned Ground Vehicle (UGV), leveraging the advantages of collaborative work for pollination, referred to as Pollinationbots. Pollinationbots is based in swarm behaviors and methodologies to implement more effective pollination strategies, ensuring efficient pollination across various scenarios. The paper presents the architecture of the Pollinationbots system, which was evaluated using the Webots simulator, focusing on path planning and follower behavior. Preliminary simulation results indicate that this is a viable solution for robotic pollination. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Segmentation of the lungs in Computed Tomography (CT) is very challenging due to changes in lung shape, size, and parenchyma pattern, as well as differences in imaging acquisition protocols. As a consequence, these models may not be robust and may decrease their performance when deployed in a clinical setting. The Continual Learning paradigm holds great promise since learning models continually acquire incoming knowledge, having the ability to adapt to changing environments. In this work, experience replay with random sampling of past data was implemented, using the original CT images and the corresponding ground-truths. Data from four different institutions were used to develop the experiments, and the models were evaluated on a cross-cohort dataset. Using raw data, the goal was to study how the datasets and their imaging patterns were related and what impact the training datasets have on one another. The catastrophic forgetting effect diminished for almost all datasets. For two of the in-domain test datasets there was forward and backward transfer, results that could be linked to a possible similarity between them. A mean DSC of 0.94 was obtained across all datasets. The results showed how the similarity or disparity between data from different institutions can influence the performance of learning models. © 2025 IEEE.