Listar por autor "Thurnhofer Hemsi, Karl"
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Analyzing Digital Image by Deep Learning for Melanoma Diagnosis
Thurnhofer Hemsi, Karl; Domínguez-Merino, Enrique
(2019-06-19)
Image classi cation is an important task in many medical applications, in order to achieve an adequate diagnostic of di erent le- sions. Melanoma is a frequent kind of skin cancer, which most of them can be detected by ... -
Aprendizaje automático para la detección de inicio/fin de la marcha mediante dos sensores inerciales y modelado de datos usando aprendizaje profundo.
(UMA Editorial, 2024)Los médicos utilizan extensamente el análisis de la marcha para detectar anormalidades y determinar posibles tratamientos para los pacientes. El análisis de la marcha tiene diversas aplicaciones, que incluyen la identificación ... -
Comparación de marcos de trabajo de Aprendizaje Profundo para la detección de objetos
Benito-Picazo, Jesús; Thurnhofer Hemsi, Karl; Molina-Cabello, Miguel Ángel; Domínguez, Enrique (2018-11-08)
Muchas aplicaciones en visión por computador necesitan de sistemas de detección precisos y eficientes. Esta demanda coincide con el auge de la aplicación de técnicas de aprendizaje profundo en casi todos las áreas del ... -
Enhanced transfer learning model by image shifting on a square lattice for skin lesion malignancy assessment
Molina-Cabello, Miguel Ángel; Thurnhofer Hemsi, Karl; Maza Quiroga, Rosa María; Domínguez, Enrique; López-Rubio, Ezequiel; Molina-Cabello, Miguel Ángel[et al.] (2021)
Skin cancer is one of the most prevalent diseases among people. Physicians have a challenge every time they have to determine whether a diseased skin is benign or malign. There exist clinical diagnosis methods (such as ... -
Ensemble ellipse fitting by spatial median consensus
Thurnhofer Hemsi, Karl; López-Rubio, Ezequiel; Blázquez-Parra, Elidia Beatriz; Ladrón-de-Guevara-Muñoz, María del Carmen; De-Cózar-Macías, Óscar (2021)
Ellipses are among the most frequently used geometric models in visual pattern recognition and digital image analysis. This work aims to combine the outputs of an ensemble of ellipse fitting methods, so that the deleterious ... -
Histopathological image analysis for breast cancer diagnosis by ensembles of convolutional neural networks and genetic algorithms
Molina-Cabello, Miguel Ángel; Rodríguez Rodríguez, José Antonio; Thurnhofer Hemsi, Karl; López-Rubio, Ezequiel (2021-07)
One of the most invasive cancer types which affect women is breast cancer. Unfortunately, it exhibits a high mortality rate. Automated histopathological image analysis can help to diagnose the disease. Therefore, computer ... -
Skin lesion classification by ensembles of deep convolutional networks and regularly spaced shifting
Thurnhofer Hemsi, Karl; López-Rubio, Ezequiel; Domínguez, Enrique; Elizondo Acuña, David Alberto (2021)
Skin lesions are caused due to multiple factors, like allergies, infections, exposition to the sun, etc. These skin diseases have become a challenge in medical diagnosis due to visual similarities, where image classification ... -
Super-resolution of 3D Magnetic Resonance Images by Random Shifting and Convolutional Neural Networks
Thurnhofer Hemsi, Karl; López-Rubio, Ezequiel; Roé-Vellvé, Núria; Domínguez-Merino, Enrique; Molina-Cabello, Miguel Ángel (IEEE, 2018)
Enhancing resolution is a permanent goal in magnetic resonance (MR) imaging, in order to keep improving diagnostic capability and registration methods. Super-resolution (SR) techniques are applied at the postprocessing ...