  The Gleason grading system using histological images is the most powerful
diagnostic and prognostic predictor of prostate cancer. The current standard
inspection is evaluating Gleason H&E-stained histopathology images by
pathologists. However, it is complicated, time-consuming, and subject to
observers. Deep learning (DL) based-methods that automatically learn image
features and achieve higher generalization ability have attracted significant
attention. However, challenges remain especially using DL to train the whole
slide image (WSI), a predominant clinical source in the current diagnostic
setting, containing billions of pixels, morphological heterogeneity, and
artifacts. Hence, we proposed a convolutional neural network (CNN)-based
automatic classification method for accurate grading of PCa using whole slide
histopathology images. In this paper, a data augmentation method named
Patch-Based Image Reconstruction (PBIR) was proposed to reduce the high
resolution and increase the diversity of WSIs. In addition, a distribution
correction (DC) module was developed to enhance the adaption of pretrained
model to the target dataset by adjusting the data distribution. Besides, a
Quadratic Weighted Mean Square Error (QWMSE) function was presented to reduce
the misdiagnosis caused by equal Euclidean distances. Our experiments indicated
the combination of PBIR, DC, and QWMSE function was necessary for achieving
superior expert-level performance, leading to the best results (0.8885
quadratic-weighted kappa coefficient).
