Providing Pathology Solutions to Deprived Areas Around the World
Posted 4th September 2019 by Liv Sewell
Most students and clinicians learn microbiology with the proper equipment: microscopes. However, in deprived countries front-line health facilities have to refer patients elsewhere because they do not have a microscope to enable diagnosis. Research is inhibited because of lack of equipment, students never get the opportunity to use real microscopes during their studies, and participation in science and particularly microbiology is very low.
Presentation slides from the 5th Digital Pathology & AI Congress: USA
Posted 28th August 2019 by Jane Williams
Following the 5th Digital Pathology & AI Congress: USA, we have made the following presentation slides available from Iman Hajirasouliha, Kim Solez and Mrinal Mandal.
What Digital Pathology can learn from Radiology
Posted 17th May 2019 by Joshua Sewell
Radiology is ahead of the curve because they’ve had CAD (Computer Aided Detection) for about 20 years. Radiology as a field has therefore had experience of introducing and integrating AI algorithms.
In my previous post, I talked about high-level cross imaging modalities. Here, I will discuss three challenges specific to pathology. I also work with Radiology imaging, and I think that comparisons between the two can help see how pathology might develop in the future.
Four challenges in developing AI algorithms for medical imaging
Posted 10th May 2019 by Joshua Sewell
Unsurprisingly, there is a lot of hype surrounding AI. Available deep learning packages make it so easy to create models and so we can expect lots of them to emerge. Anyone able to access sufficiently labelled data can start building models.
The promise of AI in medical imaging: improving medical practice
Posted 3rd May 2019 by Joshua Sewell
In our lab of Quantitative Imaging and Artificial Intelligence, we’re developing AI applications in a variety of areas, such as radiology and pathology. The goal is to develop applications that meet unmet medical needs, particularly in relation to precision medicine and clinical prediction.
PathLAKE: Benefitting Pathologists with exemplary projects
Posted 17th April 2019 by Joshua Sewell
David Snead is Consultant Histopathologist and Clinical lead for Coventry and Warwickshire Pathology Services (CWPS), a network of labs hosted by University Hospitals of Coventry and Warwickshire NHS Trust. As head of the UHCW Digital Pathology Centre of Excellence, he is now heavily involved in the Pathology image data Lake for Analytics, Knowledge, and Education (PathLAKE).
Transforming medical image analysis with deep learning AI
Posted 5th April 2019 by Joshua Sewell
Aiforia Technologies is a medical AI software company seeking to transform clinical pathology and medical research by bringing deep learning AI to assist and augment human experts in medical image analysis.
We had the chance to ask CEO Kaisa Helminen about AI in healthcare and Aiforia’s newest platform.
How Can we Develop Collaborative Standards for AI in Digital Pathology?
Posted 22nd March 2019 by Joshua Sewell
As laboratories transform their workflows into the digital environment, a tremendous opportunity presents itself: to transition the field of pathology from a qualitative to quantitative discipline. Quantitation brings measures of accuracy, reproducibility, and statistical stringency that allow computational algorithms (including AI) to perform complex tasks and measure their success. The evolution of Pathology will not be dictated by any single organization but rather will take an entire community of experts.
