  In the field of computational pathology, the use of decision support systems
powered by state-of-the-art deep learning solutions has been hampered by the
lack of large labeled datasets. Until recently, studies relied on datasets in
the order of few hundreds of slides which are not enough to train a model that
can work at scale in the clinic. Here, we have gathered a dataset consisting of
12,160 slides, two orders of magnitude larger than previous datasets in
pathology and equivalent to 25 times the pixel count of the entire ImageNet
dataset. Given the size of our dataset it is possible for us to train a deep
learning model under the Multiple Instance Learning (MIL) assumption where only
the overall slide diagnosis is necessary for training, avoiding all the
expensive pixel-wise annotations that are usually part of supervised learning
approaches. We test our framework on a complex task, that of prostate cancer
diagnosis on needle biopsies. We performed a thorough evaluation of the
performance of our MIL pipeline under several conditions achieving an AUC of
0.98 on a held-out test set of 1,824 slides. These results open the way for
training accurate diagnosis prediction models at scale, laying the foundation
for decision support system deployment in the clinic.
