Andrew Janowczyk, PhD
Department of Biomedical Engineering
Title: Computational Pathology: Towards Precision Medicine
Roughly 40% of the population will be diagnosed with some form of cancer in their lifetime. In a large majority of these cases, a definitive cancer diagnosis is only possible via histopathologic confirmation on a tissue slide. With the increasing popularity of the digitization of pathology slides, a wealth of new untapped data is now regularly being created.
Computational analysis of these routinely captured H&E slides is facilitating the creation of diagnostic tools for tasks such as disease identification and grading. Further, by identifying patterns of disease presentation across large cohorts of retrospectively analyzed patients, new insights for predicting prognosis and therapy response are possible [1,2]. Such biomarkers, derived from inexpensive histology slides, stand to improve the standard of care for all patient populations, especially where expensive genomic testing may not be readily available. Moreover, since numerous other diseases and disorders, such as oncoming clinical heart failure , are similarly diagnosed via pathology slides, those patients also stand to benefit from these same technological advances in the digital pathology space.
This talk will discuss our research aimed towards reaching the goal of precision medicine, wherein patients receive optimized treatment based on historical evidence. The talk discusses how the applications of deep learning in this domain are significantly improving the efficiency and robustness of these models . Numerous challenges remain, though, especially in the context of quality control and annotation gathering. This talk further introduces the audience to open-source tools being developed and deployed to meet these pressing needs, including quality control (histoqc.com ), annotation (quickannotator.com), labeling (patchsorter.com), validation (cohortfinder.com).