“We propose to use what’s known as weakly supervised training,” Dylov says. “Since two clearly defined classes are unavailable, this task usually tends to be treated with unsupervised or out-of-distribution models. That is, the anomalous cases are not identified as such in the training data. However, treating the anomalous class as a complete unknown is actually very strange for a clinical problem, because doctors can always point to a few anomalous examples. So, we showed some abnormal images to the network to unleash the arsenal of weakly supervised methods, and it helped a lot. Even just one anomalous scan for every 200 normal ones goes a long way, and this is quite realistic.”

According to the authors, their approach — Deep Perceptual Autoencoders — is easy to carry over to a wide range of other medical scans, beyond the two kinds used in the study, because the solution is adapted to the general nature of such images. Namely, it is sensitive to small-scale anomalies and uses few of their examples in training.


Study co-author and the director of the Philips Research branch in Moscow, Irina Fedulova commented: “We are glad that the Philips-Skoltech partnership enables us to address challenges like this one that are of great relevance to the health care industry. We expect this solution to considerably accelerate the work of histopathologists, radiologists, and other medical professionals facing the tedious task of spotting minute abnormalities in large sets of images. By subjecting the scans to preliminary analysis, the obviously unproblematic images can be eliminated, giving the human expert more time to focus on the more ambiguous cases.”

Skoltech is a private international university located in Russia. Established in 2011 in collaboration with the Massachusetts Institute of Technology (MIT), Skoltech is cultivating a new generation of leaders in the fields of science, technology, and business, conducting research in breakthrough fields, and promoting technological innovation with the goal of solving critical problems that face Russia and the world. Skoltech is focusing on six priority areas: artificial intelligence and communications, life sciences and health, cutting-edge engineering and advanced materials, energy efficiency and ESG, photonics and quantum technologies, advanced studies.

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