  Pathologists diagnose and grade prostate cancer by examining tissue from
needle biopsies on glass slides. The cancer's severity and risk of metastasis
are determined by the Gleason grade, a score based on the organization and
morphology of prostate cancer glands. For diagnostic work-up, pathologists
first locate glands in the whole biopsy core, and -- if they detect cancer --
they assign a Gleason grade. This time-consuming process is subject to errors
and significant inter-observer variability, despite strict diagnostic criteria.
This paper proposes an automated workflow that follows pathologists'
\textit{modus operandi}, isolating and classifying multi-scale patches of
individual glands in whole slide images (WSI) of biopsy tissues using distinct
steps: (1) two fully convolutional networks segment epithelium versus stroma
and gland boundaries, respectively; (2) a classifier network separates benign
from cancer glands at high magnification; and (3) an additional classifier
predicts the grade of each cancer gland at low magnification. Altogether, this
process provides a gland-specific approach for prostate cancer grading that we
compare against other machine-learning-based grading methods.
