  Prostate cancer is the most prevalent cancer among men in Western countries,
with 1.1 million new diagnoses every year. The gold standard for the diagnosis
of prostate cancer is a pathologists' evaluation of prostate tissue.
  To potentially assist pathologists deep-learning-based cancer detection
systems have been developed. Many of the state-of-the-art models are
patch-based convolutional neural networks, as the use of entire scanned slides
is hampered by memory limitations on accelerator cards. Patch-based systems
typically require detailed, pixel-level annotations for effective training.
However, such annotations are seldom readily available, in contrast to the
clinical reports of pathologists, which contain slide-level labels. As such,
developing algorithms which do not require manual pixel-wise annotations, but
can learn using only the clinical report would be a significant advancement for
the field.
  In this paper, we propose to use a streaming implementation of convolutional
layers, to train a modern CNN (ResNet-34) with 21 million parameters end-to-end
on 4712 prostate biopsies. The method enables the use of entire biopsy images
at high-resolution directly by reducing the GPU memory requirements by 2.4 TB.
We show that modern CNNs, trained using our streaming approach, can extract
meaningful features from high-resolution images without additional heuristics,
reaching similar performance as state-of-the-art patch-based and
multiple-instance learning methods. By circumventing the need for manual
annotations, this approach can function as a blueprint for other tasks in
histopathological diagnosis.
  The source code to reproduce the streaming models is available at
https://github.com/DIAGNijmegen/pathology-streaming-pipeline .
