CRIS

Permanent URI for this communityhttps://scripta.up.edu.mx/handle/20.500.12552/1

Browse

Settings

Author: search.filters.author.Renza, DiegoSubject: search.filters.subject.Biological system modeling

Search Results

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    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.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    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