  In this paper, we develop a new weakly-supervised learning algorithm to learn
to segment cancerous regions in histopathology images. Our work is under a
multiple instance learning framework (MIL) with a new formulation, deep weak
supervision (DWS); we also propose an effective way to introduce constraints to
our neural networks to assist the learning process. The contributions of our
algorithm are threefold: (1) We build an end-to-end learning system that
segments cancerous regions with fully convolutional networks (FCN) in which
image-to-image weakly-supervised learning is performed. (2) We develop a deep
week supervision formulation to exploit multi-scale learning under weak
supervision within fully convolutional networks. (3) Constraints about positive
instances are introduced in our approach to effectively explore additional
weakly-supervised information that is easy to obtain and enjoys a significant
boost to the learning process. The proposed algorithm, abbreviated as DWS-MIL,
is easy to implement and can be trained efficiently. Our system demonstrates
state-of-the-art results on large-scale histopathology image datasets and can
be applied to various applications in medical imaging beyond histopathology
images such as MRI, CT, and ultrasound images.
