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Spotting and diagnosing cancer is a complex and difficult process even for the dedicated medical professionals who do it for a living. A new tool from Google researchers could improve the process by providing what amounts to reverse image search for suspicious or known cancerous cells. But it’s more than a simple matching algorithm.
Part of the diagnosis process is often examining tissue samples under a microscope and looking for certain telltale signals or shapes that may indicate one or another form of cancer. This can be a long and arduous process because every cancer and every body is different, and the person inspecting the data must not only look at the patient’s cells but also compare them to known cancerous tissues from a database or even a printed book of samples.
Years - Images - Job - Machine - Agents
As has been amply demonstrated for years now, matching similar images to one another is a job well suited to machine learning agents. It’s what powers things like Google’s reverse image search, where you put in one picture and it finds ones that are visually similar. But this technique has also been used to automate processes in medicine, where a computer system can highlight areas of an X-ray or MRI that have patterns or features it has been trained to recognize.
That’s all well and good, but the complexity of cancer pathology rules out simple pattern recognition between two samples. One may be from the pancreas, another from the lung, for example, meaning the two situations might be completely different despite being visually similar. And an experienced doctor’s “intuition” is not to be replaced, nor would the doctor suffer it to be replaced.
Aware - Opportunities - Limitations - Google - Research
Aware of both the opportunities and limitations here, Google’s research team built SMILY (Similar Medical Images Like Yours), which is a sort of heavily augmented reverse image search built...
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