Early detection is key to surviving melanoma, a type of malignant tumor responsible for more than 70% of skin-cancer-related deaths worldwide, but “suspicious pigmented skin lesions” (SPLs) are so common it’s impractical for doctors to check them all out. Now MIT researchers have developed a tool that can analyze skin photos taken with a smartphone to determine which SPLs should be evaluated by a dermatologist.
The researchers, who include professors Martha Gray, SM ’81, PhD ’86, James Collins, and Regina Barzilay and postdoc Luis Soenksen, PhD ’20, made use of deep convolutional neural networks, machine-learning algorithms often used to classify images.
The team had dermatologists visually classify the lesions in 20,388 images from 133 patients at the Hospital Gregorio Marañón in Madrid and a number of publicly available images. The system was trained on 80% of those images and tested with the rest. It distinguished more than 90.3% of SPLs from nonsuspicious lesions, skin, and complex backgrounds. It also was able to classify the level of suspiciousness.
“Our research suggests that systems leveraging computer vision and deep neural networks, quantifying such common signs, can achieve comparable accuracy to expert dermatologists,” Soenksen says. The screenings could be done during routine primary care visits, or even by patients themselves.
Keep Reading
Most Popular
Large language models can do jaw-dropping things. But nobody knows exactly why.
And that's a problem. Figuring it out is one of the biggest scientific puzzles of our time and a crucial step towards controlling more powerful future models.
OpenAI teases an amazing new generative video model called Sora
The firm is sharing Sora with a small group of safety testers but the rest of us will have to wait to learn more.
Google’s Gemini is now in everything. Here’s how you can try it out.
Gmail, Docs, and more will now come with Gemini baked in. But Europeans will have to wait before they can download the app.
This baby with a head camera helped teach an AI how kids learn language
A neural network trained on the experiences of a single young child managed to learn one of the core components of language: how to match words to the objects they represent.
Stay connected
Get the latest updates from
MIT Technology Review
Discover special offers, top stories, upcoming events, and more.