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Now A.I. Can Spot Skin Cancer Better Than Dermatologists


A computer was better than human dermatologists at detecting skin cancer in a study that pitted human against the machine in the quest for better, faster diagnostics.

A team from Germany, the US and France taught an artificial intelligence system to distinguish dangerous skin lesions from benign ones, showing it more than 1,00,000 images.

The machine — a deep learning convolutional neural network or CNN — was then tested against 58 dermatologists from 17 countries, shown photos of malignant melanomas and benign moles. Just over half the dermatologists were at “expert” level with more than five years of experience, 19 percent had between two and five years’ experience, and 29 percent were beginners with less than two years under their belt.

CNNs use a variation of multilayer perceptrons designed to require minimal preprocessing. They are also known as shift invariant or space invariant artificial neural networks (SIANN), based on their shared-weights architecture and translation invariance characteristics.

Convolutional networks were inspired by biological processes in that the connectivity pattern between neurons resembles the organization of the animal visual cortex. Individual cortical neurons respond to stimuli only in a restricted region of the visual field known as the receptive field. The receptive fields of different neurons partially overlap such that they cover the entire visual field.

“Most dermatologists were outperformed by the CNN,” the research team wrote in a paper published in the journal Annals of Oncology.

On average, flesh and blood dermatologists accurately detected 86.6 percent of skin cancers from the images, compared to 95% for the CNN.

“The CNN missed fewer melanomas, meaning it had a higher sensitivity than the dermatologists,” the study’s first author Holger Haenssle of the University of Heidelberg said in a statement. It also “misdiagnosed fewer benign moles as malignant melanoma... this would result in less unnecessary surgery.”
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