Switch to digital pathology bears fruit
Posted 11th March 2022 by Nicholas Noakes
Fully digitising pathology operations has led to greater efficiency, cost savings, and quicker diagnosis for the Laboratory of Pathology East Netherlands (LabPON). The move, made six years ago, is showing measurable benefits and now, the institution is beginning to explore the potential of deep learning computational pathology algorithms, which might push the efficiency gains even further.
Prof Alexi Baidoshvili
Digital Pathology: The Next Hurdles
Posted 31st January 2022 by Nicholas Noakes
A Market on the Rise
There has clearly been a surge in interest for digital pathology adoption over the last two years
Conversation with Dr Hamid Tizhoosh, Founder of KIMIA Lab and Leading Expert in the Development of Unsupervised AI for Tissue Pathology
Posted 30th September 2021 by Nicholas Noakes
“What we have to learn from day one when we design these AI applications, is that pathology has to come with us. We cannot just design a network as computer scientists and then go to the pathologists just when we need to validate it. The pathologist has to be with us from the start.“
Dr Hamid Tizhoosh
Conversation with one of the founders of modern digital pathology
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
Training AI to predict outcomes for cancer patients
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.
AI use in clinical diagnosis
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.
Machine learning advances diagnostics and prognostics
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.
How deep learning is transforming medical imaging: image search
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?’