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Tag: computational pathology

Conversation with one of the founders of modern digital pathology

“Digital pathology has reached the point where if you don’t have digitized slides, you will not be able to do six out of ten things that other pathologists can do today.”

Dr. Anil Parwani

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AI use in clinical diagnosis

Deep learning tool predicts tumour expression from whole slide images

A deep learning model to predict RNA-Seq expression of tumours from whole slide images was among the industry innovations outlined at the 7th Digital Pathology and AI Congress for Europe. Created by French-American start-up Owkin, the detail of how the company’s HE2RNA model provides virtual spatialization of gene expression was detailed to online delegates by senior translational scientist Alberto Romagnoni who highlighted its use in clinical diagnosis. During his presentation, delegates heard how Owkin has collaborated with doctors, hospitals and academic institutions to develop the tool.

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Machine learning advances diagnostics and prognostics

Computerized image analysis can predict cancer outcomes

The advent of digital pathology is offering a unique opportunity to develop computerized image analysis methods to diagnose disease and predict outcomes for cancer patients from histopathology tissue sections. Such advances can help predict the risk of recurrence, disease aggressiveness and long-term survival, according to a leading expert in the field, Professor Anant Madabhushi from Case Western Reserve University in Ohio.

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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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