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Digital Pathology

Machine Learning Innovation to aid Clinical Decision Making in Pathology

“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.

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Taking biomarker research to a new level

Ahead of the 6th Digital Pathology and AI Congress: Europe in December, we are revisiting Professor Inti Zlobec’s research, presented at last year’s Congress, which is opening a whole field of digital pathology research. Zlobec’s research applies the latest digital pathology technology to produce high-quality tissue microarrays for biomarker analysis.

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Bridging the gap between pathologist and algorithm

Could AI replace pathologists? As we look forward to the 6th Digital Pathology and AI Congress: Europe on the 5th – 6th December 2019, we look back at Dr Hamid Tizhoosh’s keynote presentation from last year.

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Pixel analysis: the new era of digital pathology

Professor David Snead is leading the implementation of digital pathology for Coventry and Warwickshire Pathology Services and directs one of the five Government funded centres in the UK pioneering AI and image diagnostics integration. His main research interests lie in extending the capability of digital pathology for routine diagnostic use and the advancement and deployment of computer assisted diagnostic algorithms. In this third highlight from the conference we revisit Professor Snead’s presentation to get a clinical perspective on how deep learning and artificial intelligence (AI) are set to play an increasing role in daily practice.

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Unleashing the power of digital pathology and AI for precision medicine

We’re looking back at the highlights from the Digital Pathology and AI meeting in 2018 as we anticipate this year’s Digital Pathology and AI Congress in December. This second post in our mini-series reviews Marylin Bui’s keynote presentation where she explained how the combination of Digital Pathology and Artificial Intelligence (AI) holds huge potential for patient care.

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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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