  Considering the profound transformation affecting pathology practice, we
aimed to develop a scalable artificial intelligence (AI) system to diagnose
colorectal cancer from whole-slide images (WSI). For this, we propose a deep
learning (DL) system that learns from weak labels, a sampling strategy that
reduces the number of training samples by a factor of six without compromising
performance, an approach to leverage a small subset of fully annotated samples,
and a prototype with explainable predictions, active learning features and
parallelisation. Noting some problems in the literature, this study is
conducted with one of the largest WSI colorectal samples dataset with
approximately 10,500 WSIs. Of these samples, 900 are testing samples.
Furthermore, the robustness of the proposed method is assessed with two
additional external datasets (TCGA and PAIP) and a dataset of samples collected
directly from the proposed prototype. Our proposed method predicts, for the
patch-based tiles, a class based on the severity of the dysplasia and uses that
information to classify the whole slide. It is trained with an interpretable
mixed-supervision scheme to leverage the domain knowledge introduced by
pathologists through spatial annotations. The mixed-supervision scheme allowed
for an intelligent sampling strategy effectively evaluated in several different
scenarios without compromising the performance. On the internal dataset, the
method shows an accuracy of 93.44% and a sensitivity between positive
(low-grade and high-grade dysplasia) and non-neoplastic samples of 0.996. On
the external test samples varied with TCGA being the most challenging dataset
with an overall accuracy of 84.91% and a sensitivity of 0.996.
