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Deep Learning Algorithm Diagnoses Skin Cancer As Well As Seasoned Dermatologists (extremetech.com)
An anonymous reader quotes a report from ExtremeTech: Remember how that Google neural net learned to tell the difference between dogs and cats? It's helping catch skin cancer now, thanks to some scientists at Stanford who trained it up and then loosed it on a huge set of high-quality diagnostic images. During recent tests, the algorithm performed just as well as almost two dozen veteran dermatologists in deciding whether a lesion needed further medical attention. The algorithm is called a deep convolutional neural net. It started out in development as Google Brain, using their prodigious computing capacity to power the algorithm's decision-making capabilities. When the Stanford collaboration began, the neural net was already able to identify 1.28 million images of things from about a thousand different categories. But the researchers needed it to know a malignant carcinoma from a benign seborrheic keratosis. Dermatologists often use an instrument called a dermoscope to closely examine a patient's skin. This provides a roughly consistent level of magnification and a pretty uniform perspective in images taken by medical professionals. Many of the images the researchers gathered from the Internet weren't taken in such a controlled setting, so they varied in terms of angle, zoom, and lighting. But in the end, the researchers amassed about 130,000 images of skin lesions representing over 2,000 different diseases. They used that dataset to create a library of images, which they fed to the algorithm as raw pixels, each pixel labeled with additional data about the disease depicted. Then they asked the algorithm to suss out the patterns: to find the rules that define the appearance of the disease as it spreads through tissue. The researchers tested the algorithm's performance against the diagnoses of 21 dermatologists from the Stanford medical school, on three critical diagnostic tasks: keratinocyte carcinoma classification, melanoma classification, and melanoma classification when viewed using dermoscopy. In their final tests, the team used only high-quality, biopsy-confirmed images of malignant melanomas and malignant carcinomas. When presented with the same image of a lesion and asked whether they would "proceed with biopsy or treatment, or reassure the patient," the algorithm scored 91% as well as the doctors, in terms of sensitivity (catching all the cancerous lesions) and sensitivity (not getting false positives).
I have had both Basal and Squamous skin cancers since the 1990's and keep a close watch on my own skin. If I see anything suspicious I have a note book where I keep a note of what I saw, when and where. In some cases I will take a close up picture of it. Both Basal and Squamous cancers tend to appear and go away when they are very small and by doing this I have a record of "something" reappearing in the same location. Following the old adage that once is happenstance, twice is coincidence, but three times is most likely enemy action I will call for an appointment with my dermatologist and show them my records or pictures. For the last ten years I found every skin cancer well before the dermatologist would have seen it during an annual exam.
It did not used to be that way since for many years I had the same dermatologist or group and they got to know my skin about as well as I learned to. However, after that with almost yearly shifts in medical networks due to changes in insurance providers where I worked (always either the lowest bidder or highest campaign contributor), it got where I didn't see the same one twice until I got on Medicare. The patient-doctor relationship SHOULD be long term and more than just a diagnostic code and EMR's. I think it is going to get a LOT worse before it gets better so learn to know your own body and be assertive about your care.