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Tag: digital pathology

Digital Pathology, Computational Biology, Organ Transplants, & the Future of Medicine

Both transplant outcomes and lab methods have stagnated over the last 40 years. Ishita Moghe and Professor Kim Solez comment upon the rapidly changing landscape of medical research and the potential of digital pathology for transforming patient outcomes.

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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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Inspirata and Cirdan Announce New Technical Partnership at the Digital Pathology & AI Congress

At the 6th Digital Pathology & AI Congress, Inspirata and Cirdan announced their new technical partnership designed to empower NHS clinical laboratories to accelerate their use of digital pathology.

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3 Ways Single-Cell Sequencing and AI are Transforming Kidney Transplantation

Transplantation is now a successful therapy for end-stage renal disease (ESRD). The first successful kidney transplantation happened in Boston in 1954 and the procedure is now routine clinical practice in more than 80 countries worldwide.

Ishita Moghe and Kim Solez outline some of the major strides taken in kidney pathology and transplantation through the use of scRNA-seq and Artificial Intelligence (AI). 

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Digital Pathology Solutions for low-budget and low-tech regions of the world

Digital pathology is transforming possibilities for patient care and the nature of pathologists’ work. But it is clear that in low-income countries, access to pathology and laboratory medicine (PALM) services is very restricted and under-developed.[1] Low-income countries bear a disproportionate share of the global disease burden, yet they are so under-resourced in the pathology and laboratory services that are vital for the accurate diagnosis and treatment of disease.[2] This presents a huge issue for global health.

Given this global context, Rebecca Calder, Daniel Stevens and Zev Leifer’s poster, presented at the 5th Digital Pathology and AI Congress, is of notable significance.

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Deep Learning based detection of tumor tissue compartments improves prognostic immunoprofiling in muscle-invasive bladder cancer

Worldwide, bladder cancer (BC) is the 11th most commonly diagnosed cancer. In men, BC is the 7thmost commonly diagnosed cancer worldwide.[1]Although men are more likely to develop BC than women, women present with more advanced disease and have worse survival rates.[2]

Muscle-invasive bladder cancers (MIBC) are cancers that have grown into or through the muscle layers of the bladder wall.

Dr. Katharina Nekolla and Ansh Kapil and their team applied Deep Learning enabled pathology to better understand prognostic factors in MIBC. Here we review the significance of the research and share their original poster.

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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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