Tag Archives: Computational Pathology

Pathology Horizons: Watch And Learn

It is at a time like this when we really appreciate the work and developments that are taking place within the world of pathology and laboratory medicine. We realise just how much we depend on the pathologists and laboratory professionals for scientific answers and medical findings that are integral to many clinical decisions within healthcare organisations.

Recently, we have been looking back at speaker videos from Pathology Horizons conferences through the years and it’s amazing to see how many of these ideas are now a reality within laboratory operations. We are privileged to have had leading industry experts from across the world attend and speak at Pathology Horizons. The speakers have provided us with insightful knowledge on groundbreaking topics relating to new and emerging techniques, procedures and technologies that are driving the future of pathology.

We would like share some of these videos with you, beginning with a presentation on Computational Pathology by Dr. Liron Pantanowitz from University of Pittsburgh, USA which took place at Pathology Horizons 2017 in the beautiful Cairns, Australia. Dr. Pantanowitz is a well known Professor in the field of Pathology and Bioinformatics and is the Director of the Pathology Informatics Division at the University of Pittsburgh Medical Center (UPMC).

The presentation covers the pros and cons of computer aided diagnosis within Pathology and emphasises what is required to develop these much awaited applications. Can AI truly become a reality within Pathology? Can these deep learning tools perform the complex tasks of pathologists, and in some instances with superior accuracy?

View full presentation:

Dr. Liron Pantanowitz – Computer Aided Diagnosis in Pathology: Pros & Cons

Artificial Intelligence and Computational Pathology: Implications for Precision Medicine

With the advent of digital pathology, there is an opportunity to develop computerized image analysis methods to not just detect and diagnose disease from histopathology tissue sections, but to also attempt to predict risk of recurrence, predict disease aggressiveness and long term survival. At the Center for Computational Imaging and Personalized Diagnostics, our team has been developing a suite of image processing and computer vision tools, specifically designed to predict disease progression and response to therapy via the extraction and analysis of image-based “histological biomarkers” derived from digitized tissue biopsy specimens. These tools would serve as an attractive alternative to molecular based assays, which attempt to perform the same predictions.

The fundamental hypotheses underlying our work are that: 1) the genomic expressions detected by molecular assays manifest as unique phenotypic alterations (i.e. histological biomarkers) visible in the tissue; 2) these histological biomarkers contain information necessary to predict disease progression and response to therapy; and 3) sophisticated computer vision algorithms are integral to the successful identification and extraction of these biomarkers. We have developed and applied these prognostic tools in the context of several different disease domains including ER+ breast cancer, prostate cancer, Her2+ breast cancer, ovarian cancer, and more recently medulloblastomas. For the purposes of this talk Dr. Madabhushi will focus on their work in breast, prostate, rectal, oropharyngeal, and lung cancer.

About Dr. Anant Madabhushi

Dr. Madabhushi is the Director of the Center for Computational Imaging and Personalized Diagnostics (CCIPD). He is also the F. Alex Nason Professor II in the Departments of Biomedical Engineering, Pathology, Radiology, Radiation Oncology, Urology, General Medical Sciences, and Electrical Engineering and Computer Science at Case Western Reserve University.

Madabhushi received his Bachelors Degree in Biomedical Engineering from Mumbai University, India in 1998 and his Masters in Biomedical Engineering from the University of Texas, Austin in 2000. In 2004 he obtained his PhD in Bioengineering from the University of Pennsylvania and joined the Department of Biomedical Engineering, Rutgers University as an Assistant Professor in 2005. He was promoted to Associate Professor with Tenure in 2010. In 2012 he accepted the position of Associate Professor at Case Western Reserve University, Department of Biomedical Engineering and is currently directing a center on computational imaging and personalized diagnostics. He was promoted to full professor in 2014.

Dr. Madabhushi has authored over 150 peer-reviewed journal publications  and over 180 conferences papers and delivered over 240 invited talks and lectures both in the US and abroad. He has over 75 patents either issued or pending in the areas of medical image analysis, computer-aided diagnosis, and computer vision.  He has been the recipient of a number of awards for both research as well as teaching, including the Department of Defense New Investigator Award in Lung Cancer (2014), the Coulter Phase 1 and Phase 2 Early Career award (2006, 2008), and the Excellence in Teaching Award (2007-2009), along with a number of technology commercialization awards.  His research work has received grant funding from the National Cancer Institute (NIH), National Science Foundation, the Department of Defense, private foundations, and from Industry. 

Pathology Horizons 2019

Pathology Horizons is an annual and open CPD event organised by Cirdan to discuss what developments lie ahead in Pathology and what we can do to prepare or take advantage of these. Dr. Harewood will be joined by 13 more international industry experts with some of the topics including:

  • FHIR: transforming the pathology business relationship
  • Cancer Immunotherapy Biomarker
  • Digital Pathology
  • Light-sheet Microscopy
  • Machine Learning in Pathology
  • ctDNA testing for cancer

**Given the boutique nature of this conference places are limited, so please book early to guarantee your place.** Find out more about the speakers, agenda and registration here.