Deep Neural Networks in image processing, predictive modelling and diagnostic decisions
Posted 2nd September 2020 by Liv Sewell
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.
How will Ground Truth Colour Standardisation yield the benefits of AI in Medical Imaging?
Posted 6th November 2019 by Joshua Sewell
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.
PathLAKE: Benefitting Pathologists with exemplary projects
Posted 17th April 2019 by Joshua Sewell
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).
The key blockers to the adoption of digital pathology
Posted 14th January 2019 by Jane Williams
In the first part of this six-part blog series, we looked at the challenges facing the pathology department. The conversation then moved onto the key blockers standing in the way of the adoption of digital pathology.
If you weren’t able to make the panel discussion, you can watch the recording here.
The challenges facing the pathology department
Posted 7th January 2019 by Jane Williams
It was a pleasure to welcome key opinion leaders to a discussion on accelerating the impact of AI through 100% digitization of the pathology workflow. Chaired by Peter Hamilton, Head of Research at Philips Digital & Computational Pathology, barriers to adoption, what drives the need and how do we reap the benefits were all on the agenda.
If you weren’t able to make the panel discussion, this six-part blog series will uncover what was discussed. Alternatively, you can watch the recording here.
Tuning Algorithms to the Histology Lab
Posted 26th November 2018 by Jane Williams
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.
Ten things you need to know about going 100% digital for primary histopathology diagnosis
Posted 21st November 2018 by Jane Williams
Digital pathology is based on creating a digital replica of the glass slide, called whole slide image (WSI). This image is then viewed in a computer screen, which eliminates the need for using a microscope. Can you imagine working in a glassless environment, with better ergonomics and being able to immediately find the slide you need?
Image Based Risk Assessment in Cancer
Posted 1st June 2018 by Jane Williams
Pathologists identify and interpret the changes that characterise diseases in cells and tissues, both for the studying/understanding disease processes in general and obtaining clinically relevant information for individual patients. Historically, by examining biopsy specimens, pathologists identified whether a lesion was neoplastic, inflammatory, or some other broad category. As medicine evolved, the task evolved into identifying more specific classifications. For example, if it was not sufficient to make the diagnosis of cancer; it was necessary to identify the specific subtype and grade of cancer in order to inform treatment decisions that were becoming increasingly sophisticated.