Speaker
Pinaki Sarder
Date
Location
University of Houston
Abstract
Modern advances in digitization of histopathological slides and parallel advancements in
computer hardware have opened up new opportunities for the computational focused image
analysis. Thus far cancer pathology has been the main area of computational application,
however the study of renal pathology using computational tools is on the rise. In this talk, I
will introduce computational pathology using examples from renal pathology, and discuss
applications and challenges of applying machine learning tools for detection, segmentation,
quantification, and classification of microanatomical structures from digital renal histology
whole slide images. I will also sketch ideas for the integration of non-image metadata, and
discuss ongoing efforts for the development of an end-user web platform for disbursing
these tools to the broader community. I will conclude by discussing potential barriers that
still need to be addressed for adopting these developed tools in clinical practice.
computer hardware have opened up new opportunities for the computational focused image
analysis. Thus far cancer pathology has been the main area of computational application,
however the study of renal pathology using computational tools is on the rise. In this talk, I
will introduce computational pathology using examples from renal pathology, and discuss
applications and challenges of applying machine learning tools for detection, segmentation,
quantification, and classification of microanatomical structures from digital renal histology
whole slide images. I will also sketch ideas for the integration of non-image metadata, and
discuss ongoing efforts for the development of an end-user web platform for disbursing
these tools to the broader community. I will conclude by discussing potential barriers that
still need to be addressed for adopting these developed tools in clinical practice.