Pittsburgh Association for Computational Pathology is a special interest group formed to bring focus on developing basic computer vision and and machine learning algorithms for transmitted light and fluorescence microscopy imaging data from histopathology and highlight the translation role of computational pathology research in interfacing with genomics, personalized medicine and microbiomics.
Our goal is to promote a vibrant community of computational pathology research and education practices here in Pittsburgh by engaging researchers and clinicians across Pitt, CMU and Duquesne campuses, and the local industrial partners.
Pathology is the medical specialty tasked with providing an accurate mapping from tissue samples to diagnoses and treatment strategies. The integration of recent advances in artificial intelligence, large-scale computing and genomics offers new opportunities to use computation to make pathologic diagnoses faster, more accurate, and more informative. These developments may transform the way pathology is used in basic and translational research, as well as in clinical medicine. In this talk, I will discuss my lab's work in this area, including: the development of approaches to discover epithelial and stromal factors contributing to breast carcinogenesis; and the development of a deep learning-based system for the identification of metastatic cancer in breast sentinel lymph node biopsies, which won both contests in the Camelyon-2016 ISBI Grand Challenge.
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