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 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.
Posted 17th June 2020 by Liv Sewell
Almost five years ago, the plan to implement a wide-ranging digital pathology approach across the Netherlands began to take shape. As more labs across the country acquire digital pathology capability, with steps to create a strong and accessible image repository and a national image exchange platform, one of the project leaders, Professor Katrien Grünberg, offered an update and spoke of some challenges that still lie ahead.
Posted 10th June 2020 by Liv Sewell
Bringing molecular and digital pathology closer together through a more integrative approach can lead to clear advantages for diagnostic and research workflows.
Posted 3rd June 2020 by Liv Sewell
Computational pathology has increased applications for diagnosis, prediction of prognosis and therapy response, facilitating the movement of healthcare towards personalised medicine.
Coupled with deep learning, such tools are ever more efficient and robust within research and clinical settings.
Posted 4th May 2020 by Liv Sewell
When compared to traditional methods, digital pathology may seem totally superfluous, or interesting but not all that essential. And since the costs for the elegance of digital pathology solutions are not trivial in comparison to what the peddlers traditionally have offered, it is not surprising to see the adoption curve lagging. Where then is the ‘burning platform’ that can truly drive greater adoption of digital pathology?