While the original Google blog story and subsequent articles and interviews on the matter cite that “Pathologists are responsible for reviewing all the biological tissues visible on a slide. However, there can be many slides per patient, each of which is 10+ gigapixels when digitized at 40X magnification. Imagine having to go through a thousand 10 megapixel (MP) photos, and having to be responsible for every pixel. Needless to say, this is a lot of data to cover, and often time is limited.” The Motley Fool provides what they mention as “Radiology technician reviewing mammography results” and “
Google points out as well that these technologies will allow pathologists to work more efficiently, fiWoman reviewing X-Rays on lighted panel” for their photo captions in their article.nd the needle in the haystack type approach, but it will still be up to the pathologist for the final determination. Not what The Motley Fool claims however though, instead, not only do the stock images that are in this article show radiologists, or radiology technicians examining radiology studies, but one of them is looking at head CT images printed on pieces of plastic on something called a lighted panel!
In the follow up piece to this when thousands of pathologists are unemployed forced to go back into primary care or construction or blogging full time, can you at least include a picture of a pathologist with a glass slide and a microscope or a lab coat or Erlenmeyer flask with a blue liquid in it or something other than a head CT examination on plastic on a lighted panel when writing about AI in pathology for cancer diagnoses?!?
Artificial intelligence is tailor-made for sifting through massive amounts of data to find patterns. GoogLeNet AI just used image recognition and bested human doctors at detecting breast cancer.
The science of deep learning, a sub-discipline of artificial intelligence (AI), is only a recent development in the grand scheme of things, but during its short existence, it has been producing some impressive technological achievements. Advances in image recognition, language understanding, and translation have led to the development of virtual assistants, smart home speakers, and gains in cybersecurity, and they are leading the charge toward autonomous driving. Now, companies have found a way to use those AI smarts to fight cancer.Deep learning involves the construction of artificial neural networks, using software and complex algorithms to recreate the capacity of the human brain to learn. These learning computers have a particular knack for sifting through vast amounts of data and recognizing patterns, getting smarter as they go. The first breakthrough involved feeding a system thousands of pictures of cats until the program was able to recognize a cat on its own.