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Digital Pathology

Digital Pathology and Deep Learning: AI assisting in PD-L1 scoring

Ahead of this year’s Digital Pathology and AI Congress in December we look back at some of the highlights from last year.

First up, Michel Vandenberghe’s presentation on a new deep learning algorithm, which demonstrates the potential of artificial Intelligence (AI) to support pathologists, has been developed for PD-L1 scoring in tumour cells and immune cells in urothelial carcinoma samples.

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Falling off the cliff: are we short of pathologists?

I can recall clearly the pathologists coming to lecture our medical school class our second year in 1993.

Most of them felt compelled to tell us, “Pathology is a lot of fun, you can make a good living, but don’t go into it, there aren’t any jobs.”

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A High impact Open-Source tool for Digital Pathology Quality Control

Ahead of his presentation at the 6th Digital Pathology and AI Congress: Europe, we spoke to Andrew Janowczyk about his work creating scalable data analysis techniques to facilitate cancer research through the development of open-source Digital Pathology tools.

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Providing Pathology Solutions to Deprived Areas Around the World

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.

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Presentation slides from the 5th Digital Pathology & AI Congress: USA

Following the 5th Digital Pathology & AI Congress: USA, we have made the following presentation slides available from Iman Hajirasouliha, Kim Solez and Mrinal Mandal.

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What Digital Pathology can learn from Radiology

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.

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Four challenges in developing AI algorithms for medical imaging

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.

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The promise of AI in medical imaging: improving medical practice

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.

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