Automatic breast density classification using a convolutional neural network architecture search procedure

Pablo Fonseca, Julio Mendoza, Jacques Wainer, Jose Ferrer, Joseph Pinto, Jorge Guerrero, Benjamin Castaneda

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

49 Citas (Scopus)

Resumen

Breast parenchymal density is considered a strong indicator of breast cancer risk and therefore useful for preventive tasks. Measurement of breast density is often qualitative and requires the subjective judgment of radiologists. Here we explore an automatic breast composition classification workflow based on convolutional neural networks for feature extraction in combination with a support vector machines classifier. This is compared to the assessments of seven experienced radiologists. The experiments yielded an average kappa value of 0.58 when using the mode of the radiologists' classifications as ground truth. Individual radiologist performance against this ground truth yielded kappa values between 0.56 and 0.79.

Idioma originalInglés
Título de la publicación alojadaMedical Imaging 2015
Subtítulo de la publicación alojadaComputer-Aided Diagnosis
EditoresLubomir M. Hadjiiski, Lubomir M. Hadjiiski, Georgia D. Tourassi, Georgia D. Tourassi
EditorialSPIE
ISBN (versión digital)9781628415049, 9781628415049
DOI
EstadoPublicada - 2015
EventoSPIE Medical Imaging Symposium 2015: Computer-Aided Diagnosis - Orlando, Estados Unidos
Duración: 22 feb. 201525 feb. 2015

Serie de la publicación

NombreProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volumen9414
ISSN (versión impresa)1605-7422

Conferencia

ConferenciaSPIE Medical Imaging Symposium 2015: Computer-Aided Diagnosis
País/TerritorioEstados Unidos
CiudadOrlando
Período22/02/1525/02/15

Huella

Profundice en los temas de investigación de 'Automatic breast density classification using a convolutional neural network architecture search procedure'. En conjunto forman una huella única.

Citar esto