Wide and Deep neural network model for patch aggregation in CNN-based prostate cancer detection systems
|Durán López, Lourdes
Domínguez Morales, Juan Pedro
Gutiérrez Galán, Daniel
Ríos Navarro, José Antonio
Jiménez Fernández, Ángel Francisco
Vicente Díaz, Saturnino
Linares Barranco, Alejandro
|Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores
|Prostate cancer (PCa) is one of the most commonly diagnosed cancer and one of the leading causes of death among men, with almost 1.41 million new cases and around 375,000 deaths in 2020. Artificial Intelligence algorithms ...
Prostate cancer (PCa) is one of the most commonly diagnosed cancer and one of the leading causes of death among men, with almost 1.41 million new cases and around 375,000 deaths in 2020. Artificial Intelligence algorithms have had a huge impact on medical image analysis, including digital histopathology, where Convolutional Neural Networks (CNNs) are used to provide a fast and accurate diagnosis, supporting experts in this task. To perform an automatic diagnosis, prostate tissue samples are first digitized into gigapixel-resolution whole-slide images. Due to the size of these images, neural networks cannot use them as input and, therefore, small subimages called patches are extracted and predicted, obtaining a patch-level classification. In this work, a novel patch aggregation method based on a custom Wide & Deep neural network model is presented, which performs a slide-level classification using the patch-level classes obtained from a CNN. The malignant tissue ratio, a 10-bin malignant probability histogram, the least squares regression line of the histogram, and the number of malignant connected components are used by the proposed model to perform the classification. An accuracy of 94.24% and a sensitivity of 98.87% were achieved, proving that the proposed system could aid pathologists by speeding up the screening process and, thus, contribute to the fight against PCa.
|Agencia Estatal de Investigación. España
European Union (UE). H2020
Junta de Andalucía
CHIST-ERA SMALL (PCI2019-111841-2)
|Durán López, L., Domínguez Morales, J.P., Gutiérrez Galán, D., Ríos Navarro, J.A., Jiménez Fernández, Á.F., Vicente Díaz, S. y Linares Barranco, A. (2021). Wide and Deep neural network model for patch aggregation in CNN-based prostate cancer detection systems. Computers in Biology and Medicine, 136 (art. nº 104743)