  Background: Histopathology is an important modality for the diagnosis and
management of many diseases in modern healthcare, and plays a critical role in
cancer care. Pathology samples can be large and require multi-site sampling,
leading to upwards of 20 slides for a single tumor, and the human-expert tasks
of site selection and and quantitative assessment of mitotic figures are time
consuming and subjective. Automating these tasks in the setting of a digital
pathology service presents significant opportunities to improve workflow
efficiency and augment human experts in practice. Approach: Multiple
state-of-the-art deep learning techniques for histopathology image
classification and mitotic figure detection were used in the development of
OncoPetNet. Additionally, model-free approaches were used to increase speed and
accuracy. The robust and scalable inference engine leverages Pytorch's
performance optimizations as well as specifically developed speed up techniques
in inference. Results: The proposed system, demonstrated significantly improved
mitotic counting performance for 41 cancer cases across 14 cancer types
compared to human expert baselines. In 21.9% of cases use of OncoPetNet led to
change in tumor grading compared to human expert evaluation. In deployment, an
effective 0.27 min/slide inference was achieved in a high throughput veterinary
diagnostic pathology service across 2 centers processing 3,323 digital whole
slide images daily. Conclusion: This work represents the first successful
automated deployment of deep learning systems for real-time expert-level
performance on important histopathology tasks at scale in a high volume
clinical practice. The resulting impact outlines important considerations for
model development, deployment, clinical decision making, and informs best
practices for implementation of deep learning systems in digital histopathology
practices.
