Browsing by Subject "deep learning"

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    Automated Detection of Corneal Edema With Deep Learning-Assisted Second Harmonic Generation Microscopy
    (Institute of Electrical and Electronics Engineers, 2023-11-06) Anton, Stefan; Martínez Ojeda, Rosa M.; Hristu, Radu; Stanciu, George A.; Toma, Antonela; Banica, Cosmin K.; Fernández, Enrique J.; Huttunen, Mikko J.; Bueno, Juan M.; Stanciu, Stefan G.; Stanciu, Stefan G.; Bueno, Juan M.; Física
    Second Harmonic Generation Microscopy (SHG) is widely acknowledged as a valuable non-linear optical imaging tool, its contrast mechanism providing the premises to non-invasively identify, characterize, and monitor changes in the collagen architecture of tissues.However, the interpretation ofSHGdata can pose difficulties even for experts histopathologists, which represents a bottleneck for the translation of SHG-based diagnostic frameworks to clinical settings. The use of artificial intelligence methods for automated SHG analysis is still in an early stage, with only few studies having been reported to date, none addressing ocular tissues yet. In this work we explore the use of three Deep Learning models, the highly popular InceptionV3 and ResNet50, alongside FLIMBA, a custom developed architecture, requiring no pre-training, to automatically detect corneal edema in SHG images of porcine cornea. We observe that Deep Learning models building on different architectures provide complementary results for the classification ofcornea SHG images and demonstrate an AU-ROC=0.98 for their joint use. These results have potential to be extrapolated to other diagnostics scenarios, such as automated extraction of hydration level of cornea, or identification of corneal edema causes, and thus pave the way for novel methods for precision diagnostics of the cornea with Deep-Learning assisted SHG imaging.
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    Surrogate-assisted and filter-based multi-objective evolutionary feature selection for deep learning
    (Institute of Electrical and Electronics Engineers, 2023-01-12) Espinosa Fernández, Raquel; Jiménez Barrionuevo, Fernando; Palma Méndez, José Tomás; Ingeniería de la Información y las Comunicaciones
    Feature selection for deep learning prediction mod- els is a difficult topic for researchers to tackle. Most of the ap- proaches proposed in the literature consist of embedded methods through the use of hidden layers added to the neural network architecture that modify the weights of the units associated with each input attribute so that the worst attributes have less weight in the learning process. Other approaches used for deep learning are filter methods, which are independent of the learning algorithm, which can limit the precision of the prediction model. Wrapper methods are impractical with deep learning due to their high computational cost. In this paper, we propose new attribute subset evaluation feature selection methods for deep learning of the wrapper, filter and wrapper-filter hybrid types, where multi-objective and many-objective evolutionary algorithms are used as search strategies. A novel surrogate-assisted approach is used to reduce the high computational cost of the wrapper-type objective function, while the filter-type objective functions are based on correlation and an adaptation of the reliefF algorithm. The proposed techniques have been applied in a time series forecasting problem of air quality in the Spanish south-east and an indoor temperature forecasting problem in a domotic house, with promising results compared to other feature se