RT Journal Article
T1 PROMETEO: A CNN-Based Computer-Aided Diagnosis System for WSI Prostate Cancer Detection
A1 Duran-Lopez, Lourdes
A1 Dominguez-Morales, Juan P.
A1 Felix Conde-Martin, Antonio
A1 Vicente-Diaz, Saturnino
A1 Linares-Barranco, Alejandro
K1 Convolutional neural networks
K1 computer-aided diagnosis
K1 deep learning
K1 medical image analysis
K1 prostate cancer
K1 whole-slide images
K1 Biopsies
K1 Classification
K1 Normalization
AB Prostate cancer is currently one of the most commonly-diagnosed types of cancer among males. Although its death rate has dropped in the last decades, it is still a major concern and one of the leading causes of cancer death. Prostate biopsy is a test that confirms or excludes the presence of cancer in the tissue. Samples extracted from biopsies are processed and digitized, obtaining gigapixel-resolution images called whole-slide images, which are analyzed by pathologists. Automated intelligent systems could be useful for helping pathologists in this analysis, reducing fatigue and making the routine process faster. In this work, a novel Deep Learning based computer-aided diagnosis system is presented. This system is able to analyze whole-slide histology images that are first patch-sampled and preprocessed using different filters, including a novel patch-scoring algorithm that removes worthless areas from the tissue. Then, patches are used as input to a custom Convolutional Neural Network, which gives a report showing malignant regions on a heatmap. The impact of applying a stain-normalization process to the patches is also analyzed in order to reduce color variability between different scanners. After training the network with a 3-fold cross-validation method, 99.98% accuracy, 99.98% F1 score and 0.999 AUC are achieved on a separate test set. The computation time needed to obtain the heatmap of a whole-slide image is, on average, around 15 s. Our custom network outperforms other state-of-the-art works in terms of computational complexity for a binary classification task between normal and malignant prostate whole-slide images at patch level.
PB Ieee-inst electrical electronics engineers inc
SN 2169-3536
YR 2020
FD 2020-01-01
LK http://hdl.handle.net/10668/18971
UL http://hdl.handle.net/10668/18971
LA en
DS RISalud
RD Apr 11, 2025