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Digital Pathology

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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Ten things you need to know about going 100% digital for primary histopathology diagnosis

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?

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The Benefits of Computational Pathology

The histological assessment of human tissue has emerged as a key challenge for the detection and treatment of cancer. Many tissue sections have to be processed in order to find those that contain cancer, which can be a timely and costly process. Similarly, procedures such as immunohistochemical scoring can be problematic for cases such as ER, PR, and HER2.

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Saving Money with the Foldscope in Digital Pathology

At the recent Digital Pathology and AI Congress, Rebecca Calder and Daniel Stevens presented their research in the poster entitled: Preliminary Studies in the Use of the Foldscope Paper Microscope for Diagnostic Analysis of Crystals in Urine: Issues in the Analysis of Liquid Samples and Potential Applications in Low Budget/Low Tech Regions of the World. You can view the poster here.

Dr. Zev Leifer, who oversaw the project, describes the rationale.

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The 7 Steps to Digital Pathology

“There’s nothing wrong with the microscope.”

“Our Information Systems won’t integrate with other technology.”

“Change is hard.”

These are a couple of the concerns pathologists have about digital pathology. But as they learn more, pathologists are finding that their apprehension about going digital is holding them back from reaping significant benefits. They are also discovering that old methods can’t compete against today’s digital solutions.

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Truth and Fictions, Stances and Beliefs about Artificial Intelligence

At the Digital Pathology and AI Conference in New York City, it was interesting to consider the different beliefs represented about Artificial Intelligence.

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Distinction of Benign and Malignant Lesions through Deep Learning

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.

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Game Winning Hits: Singles or Home Runs?

To use a baseball analogy – there have been more games won with singles than with home runs. Home runs, when they happen, are spectacular, but they are few and far between. The journeyman single, while not the stuff of legend, gets the job done, and happens frequently. When it comes to the practical application of artificial intelligence in digital pathology, the analogy holds. The home run is machine diagnosis – replacement of the pathologist – the holy grail. The single represents baby steps which improve pathology practice, right up until the technology reaches its inevitable maturity.

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