Posted 9th March 2020 by Liv Sewell
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.
Deep Learning based detection of tumor tissue compartments improves prognostic immunoprofiling in muscle-invasive bladder cancer
Posted 11th November 2019 by Liv Sewell
Worldwide, bladder cancer (BC) is the 11th most commonly diagnosed cancer. In men, BC is the 7thmost commonly diagnosed cancer worldwide.Although men are more likely to develop BC than women, women present with more advanced disease and have worse survival rates.
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.
Posted 16th October 2019 by Liv Sewell
Could AI replace pathologists? As we look forward to the 6th Digital Pathology and AI Congress: Europe on the 5th – 6th December 2019, we look back at Dr Hamid Tizhoosh’s keynote presentation from last year.
Posted 10th May 2019 by Joshua Sewell
Unsurprisingly, there is a lot of hype surrounding AI. Available deep learning packages make it so easy to create models and so we can expect lots of them to emerge. Anyone able to access sufficiently labelled data can start building models.
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.
Posted 6th August 2018 by Jane Williams
Machine learning is already prevalent in many industries and most pathologists are unaware how accessible machine learning is and how it can be used to augment their work or research. Applications include decision support, image analytics, process improvement, disease diagnosis and prognosis.
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.