Search Results

Now showing 1 - 3 of 3

Some of the metrics are blocked by your
consent settings
A Deep-Learning and Bio-inspired Vision Model-Based Approach for Automatic Coronary Arteries Segmentation
(IEEE, 2025)
López-Figueroa, Alberto
;
Brieva, Jorge
;
Moya-Albor, Ernesto
;
Gomez-Coronel, Sandra L.
;
Renza, Diego
The accurate segmentation of coronary arteries from X-ray angiograms is critical for the diagnosis and treatment of cardiovascular diseases. Yet, it remains a challenging task due to low image contrast and complex vessel structures. This paper introduces a novel hybrid methodology that combines a bio-inspired vision model, the Steered Hermite Transform (SHT), with a deep learning architecture for robust vessel segmentation. We leverage the SHT to decompose each angiogram into a rich, multi-resolution set of 15 feature maps that capture local image structures at different scales and orientations. These Hermite coefficients, along with the original image, form a 16-channel input tensor used to train a U-Net. This approach enables the network to learn from an enhanced feature space that explicitly represents vessel-like patterns. Evaluated on a public dataset of 134 coronary angiograms, our model demonstrates outstanding performance, achieving an Area Under the Curve (AUC) of 0.9872. The results confirm that enriching the input of a deep neural network with SHT coefficients significantly improves its ability to identify and segment complex vascular networks accurately. ©The authors ©IEEE.
Some of the metrics are blocked by your
consent settings
Flexible Workplaces and Post-Pandemic Business Challenges: An Analysis of Latin American Workers by Gender, Age and Socioeconomic Level
(Institute of Electrical and Electronics Engineers (IEEE), 2025)
Cabrera-García, Victoria
;
Campos-García, Ximena
;
Acuña-Arango, Lina María
;
Docal-Millán, María del Carmen
;
Riveros-Munévar, Fernando
The COVID-19 pandemic accelerated labor flexibility and the rise of innovative business models, driven by digital transformation and the need for adaptability. This shift necessitated the development of new models, policies, and guidelines to address global expansion and work-life balance, presenting organizations with novel challenges. This study investigated work-life balance, resilience, and coping strategies of 3302 workers across five Latin American countries (Argentina, Perú, Colombia, Honduras, and México) in the context of these emerging flexible work arrangements. The findings indicate that 38.5% of workers experienced improved work-life balance with remote or hybrid work, and 49.5% reported increased productivity. However, challenges emerged, with 34.1% citing difficulties in time management, 39.7% reporting blurred work-life boundaries, and 36.9% finding it harder to take breaks. Gender differences were observed in resilience and coping mechanisms: men exhibited higher resilience, favored cognitive restructuring and spiritual support, while women relied more on family support and acceptance of help. Notably, no significant gender differences were found in work-life balance or social support. The study suggests that flexible work modalities positively influence employee well-being. However, this flexibility necessitates a reevaluation of business and operational models, including a thorough analysis of work-life balance conditions, considering employees' resilience and coping strategies within the specific productive and organizational context. © The authors © IEEE.
Some of the metrics are blocked by your
consent settings
Evaluation of Dataset Distribution in Biomedical Image Classification Against Image Acquisition Distortions
(IEEE, 2024)
Aguilera-González, Santiago
;
Renza, Diego
;
Moya-Albor, Ernesto
One of the conditions expected when training a machine learning model is that the inference data should be independently and identically distributed (i.i.d.) with respect to the training data. However, as the real world evolves, this condition can be lost, which is known as shift distribution. This situation can affect the performance of a machine learning model, so the question is how to evaluate (without training a model) the presence of shift distribution. Consequently, this paper presents a proposal to determine the degree of distribution shift in medical image datasets in the face of possible distortions due to the capture system. The methodology is based on Cumulative Spectral Gradient (CSG) metric and it is applied to three biomedical imaging datasets extracted from MedMNIST, an initiative that has compiled several standardized biomedical datasets: PneumoniaMNIST, BreastMNIST and RetinaMNIST. Thanks to this methodology, it is possible to evaluate which types of modifications have a greater impact on the generalization of the models, as well as to determine if there are classes more affected by corruptions. ©The authors ©IEEE.
6

CRIS

Browse

Filters

Settings

Sort By

Results per page

Search Results