Posted 31st August 2021 by Nicholas Noakes
“Digital pathology has reached the point where if you don’t have digitized slides, you will not be able to do six out of ten things that other pathologists can do today.”
Dr. Anil Parwani
Posted 25th May 2021 by Nicholas Noakes
Andrew Evans, speaking at the Digital Pathology & AI Congress USA, described new guidelines he helped to draft for validating whole-slide-imaging for diagnostic purposes. First published in 2013 the guidelines were designed to address the fundamental question, “what needs to be done to validate a whole slide imaging for diagnostic use?”. The review producing the new guidelines was published in May 2021.
Posted 30th March 2021 by Nicholas Noakes
Predicting the outcome of cancer can help the clinical decision-making process related to a patient’s treatment. The potential for Artificial Intelligence (AI) to support this was a key facet of the final keynote speech to the online 7th Digital Pathology and AI Congress: Europe, by Johan Lundin, Research Director at the Institute for Molecular Medicine Finland (FIMM) at the University of Helsinki and Professor of Medical Technology at Karolinska Institutet.
Posted 19th February 2021 by Nicholas Noakes
Deep learning tool predicts tumour expression from whole slide images
A deep learning model to predict RNA-Seq expression of tumours from whole slide images was among the industry innovations outlined at the 7th Digital Pathology and AI Congress for Europe. Created by French-American start-up Owkin, the detail of how the company’s HE2RNA model provides virtual spatialization of gene expression was detailed to online delegates by senior translational scientist Alberto Romagnoni who highlighted its use in clinical diagnosis. During his presentation, delegates heard how Owkin has collaborated with doctors, hospitals and academic institutions to develop the tool.
Posted 10th February 2021 by Nicholas Noakes
Computerized image analysis can predict cancer outcomes
The advent of digital pathology is offering a unique opportunity to develop computerized image analysis methods to diagnose disease and predict outcomes for cancer patients from histopathology tissue sections. Such advances can help predict the risk of recurrence, disease aggressiveness and long-term survival, according to a leading expert in the field, Professor Anant Madabhushi from Case Western Reserve University in Ohio.
Posted 2nd September 2020 by Liv Sewell
Now more than ever intelligent human beings are needed to fulfil high value and high complexity tasks and histopathology is no exception. In fact, it is one of the sectors in the most desperate need.
Posted 29th July 2020 by Liv Sewell
Five or six years ago, I was looking for a strategic direction for the KIMIA Lab. We started with the problems. We asked, ‘What problems do we have in medical imaging?’
Posted 15th July 2020 by Liv Sewell
It was fascinating to speak with Hamid Tizhoosh, Professor at the Faculty of Engineering at the University of Waterloo in Canada, Director of KIMIA Lab and keynote speaker at the 6th Digital Pathology & AI Congress: USA, about using AI to transform what is possible in medical imaging.