  While microscopic analysis of histopathological slides is generally
considered as the gold standard method for performing cancer diagnosis and
grading, the current method for analysis is extremely time consuming and labour
intensive as it requires pathologists to visually inspect tissue samples in a
detailed fashion for the presence of cancer. As such, there has been
significant recent interest in computer aided diagnosis systems for analysing
histopathological slides for cancer grading to aid pathologists to perform
cancer diagnosis and grading in a more efficient, accurate, and consistent
manner. In this work, we investigate and explore a deep triple-stream residual
network (TriResNet) architecture for the purpose of tile-level histopathology
grading, which is the critical first step to computer-aided whole-slide
histopathology grading. In particular, the design mentality behind the proposed
TriResNet network architecture is to facilitate for the learning of a more
diverse set of quantitative features to better characterize the complex tissue
characteristics found in histopathology samples. Experimental results on two
widely-used computer-aided histopathology benchmark datasets (CAMELYON16
dataset and Invasive Ductal Carcinoma (IDC) dataset) demonstrated that the
proposed TriResNet network architecture was able to achieve noticeably improved
accuracies when compared with two other state-of-the-art deep convolutional
neural network architectures. Based on these promising results, the hope is
that the proposed TriResNet network architecture could become a useful tool to
aiding pathologists increase the consistency, speed, and accuracy of the
histopathology grading process.
