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 6th November 2019 by Joshua Sewell
The essence of colour management is to ensure that the original target translates through a digital pathway so that the output images are exactly the same colour as the original.
When applied to medical imaging, and considering all the specific stains used in pathology, colour management becomes important. Particular coloured stains bind specific structures of cells in tissue to confer visualisation of diagnostic information. Without translating the colour through the digital pathway correctly, you lose the aspect of diagnostic information, which comes from specific colours.
Posted 1st November 2019 by Joshua Sewell
“A medical image alone doesn’t add value unless it is tied to a clinical decision-making process” – Jochen Lennerz
The Breast Cancer Scanning Initiative (BCSI) scans histological slides of patients with high-risk breast lesions to generate annotated images. We then apply a Machine Learning algorithm to the slides to determine whether surgery is necessary.
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.
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.
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.