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Digital Pathology

Guidelines for Validating Whole Slide Imaging for Diagnostic Purposes

Andrew Evans, speaking at the Digital Pathology & AI Congress USA, described new guidelines he helped to draft for validating whole-slide-imaging for diagnostic purposes. First published in 2013 the guidelines were designed to address the fundamental question, “what needs to be done to validate a whole slide imaging for diagnostic use?”. The review producing the new guidelines was published in May 2021.

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Training AI to predict outcomes for cancer patients

Predicting the outcome of cancer can help the clinical decision-making process related to a patient’s treatment. The potential for Artificial Intelligence (AI) to support this was a key facet of the final keynote speech to the online 7th Digital Pathology and AI Congress: Europe, by Johan Lundin, Research Director at the Institute for Molecular Medicine Finland (FIMM) at the University of Helsinki and Professor of Medical Technology at Karolinska Institutet.

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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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Telepathology for second opinion teleconsultation

Speaking at the 2020 Digital Pathology Congress Liron Pantanowiz reviewed why he thought telepathology is the number one application for digital imaging. In this blog, we report what he had to say about telepathology for second opinion teleconsultation.

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Telepathology in the acute care setting

Speaking at the 2020 Digital Pathology Congress Liron Pantanowiz reviewed why he thought telepathology is still one of the key applications for digital imaging. In this blog, we report what he had to say about telepathology in an acute care setting and the importance of validation.

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Deep Neural Networks in image processing, predictive modelling and diagnostic decisions

Now more than ever intelligent human beings are needed to fulfil high value and high complexity tasks and histopathology is no exception. In fact, it is one of the sectors in the most desperate need. 

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How deep learning is transforming medical imaging: image search

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?’ 

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