  Diagnostic, prognostic and therapeutic decision-making of cancer in pathology
clinics can now be carried out based on analysis of multi-gigapixel tissue
images, also known as whole-slide images (WSIs). Recently, deep convolutional
neural networks (CNNs) have been proposed to derive unsupervised WSI
representations; these are attractive as they rely less on expert annotation
which is cumbersome. However, a major trade-off is that higher predictive power
generally comes at the cost of interpretability, posing a challenge to their
clinical use where transparency in decision-making is generally expected. To
address this challenge, we present a handcrafted framework based on deep CNN
for constructing holistic WSI-level representations. Building on recent
findings about the internal working of the Transformer in the domain of natural
language processing, we break down its processes and handcraft them into a more
transparent framework that we term as the Handcrafted Histological Transformer
or H2T. Based on our experiments involving various datasets consisting of a
total of 5,306 WSIs, the results demonstrate that H2T based holistic WSI-level
representations offer competitive performance compared to recent
state-of-the-art methods and can be readily utilized for various downstream
analysis tasks. Finally, our results demonstrate that the H2T framework can be
up to 14 times faster than the Transformer models.
