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Tag: machine learning

How will Ground Truth Colour Standardisation yield the benefits of AI in Medical Imaging?

The essence of colour management is to ensure that the original target translates through a digital pathway so that the output images are exactly the same colour as the original.

When applied to medical imaging, and considering all the specific stains used in pathology, colour management becomes important. Particular coloured stains bind specific structures of cells in tissue to confer visualisation of diagnostic information. Without translating the colour through the digital pathway correctly, you lose the aspect of diagnostic information, which comes from specific colours.

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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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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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The future of Pharma: harnessing AI to decentralise data

 

As Chief Data Officer for the OSTHUS Group, Eric Little co-founded LeapAnalysis, a new approach to AI, data integration and analytics. 

 

LeapAnalysis is the first fully federated and virtualised search and analytics engine that runs on semantic metadata. It allows users to combine semantic models (ontologies) with machine learning algorithms to provide customers with unparalleled flexibility in utilizing their data.

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Slides from the Plant Genomics & Gene Editing Congress: Europe

The latest event in the Plant Genomics series provided two days of intriguing topics and case studies, not least concerning the ECJ ruling on gene editing and Machine Learning techniques in plant genomics. We have made the following presentation slides available from Ian Bancroft, Anna Coll, Aalt-Jan van Dijk, Nigel Halford, and Jurriaan Ton.

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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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Tuning Algorithms to the Histology Lab

The promise of an effective set of tools based on deep learning or other machine learning algorithms is the current buzz of the digital pathology markets. While the evolving tools, models and techniques are producing strongly positive results, there are still many factors which impact the utility and portability of models and tools being created across real-world data sets.

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Human Genomic Evidence: Revolutionising the identification and prioritisation of targets for better medicines

Nine out of 10 potential drugs that enter clinical trials never make it to the market. Failure often occurs because the biological target chosen is not well understood. However, it is hard to objectively select targets with a high chance of clinical success because the data required to predict efficacy and safety are complex, dispersed and incomplete. To address this challenge, Open Targets was founded in 2014 as a public-private partnership by GSK, EMBL-EBI and the Wellcome Sanger Institute. The consortium has grown since its launch, welcoming new partners Biogen in 2016, Takeda in 2017, and Celgene in 2018.

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