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Tag: computational pathology

Computational pathology: Heading for personalised medicine

Computational pathology has increased applications for diagnosis, prediction of prognosis and therapy response, facilitating the movement of healthcare towards personalised medicine.

Coupled with deep learning, such tools are ever more efficient and robust within research and clinical settings.

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Developing computationally derived imaging biomarkers for maximum clinical impact

Professor Anant Madabhushi is a world-recognised, award-winning leader in computerized imaging research and translational applications, with over 160 peer-reviewed journal publications and close to 100 patents issued or pending. He is a keynote speaker at the 6th Digital Pathology & AI Congress: USA. He explains here why having patents is not enough…

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Developing Deep Learning Models for Pathology Analysis

Ahead of the 6th Digital Pathology & AI Congress: USA, Dr Saeed Hassanpour introduces us to the subject of his presentation: the opportunities and challenges in developing deep learning based tools for histology.

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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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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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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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PathLAKE: Benefitting Pathologists with exemplary projects

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).

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