  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 in 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.
