Source: Psychology Today | March 7, 2019
Author: Cami Rosso
Mount Sinai creates novel deep learning system for neurodegenerative diseases
Recently, a team led by pioneering researchers from Mount Sinai School of Medicine in New York City created one of the first platforms using large-scale image data in neuropathology for building and evaluating deep learning algorithms.
In a study published a few weeks ago in Nature’s Laboratory Investigation, the official journal of the United States and Canadian Academy of Pathology, Mount Sinai researchers developed a new deep learning algorithm using convolutional neural networks. The algorithm can recognize, classify, and quantify diagnostic elements of tauopathies—neurodegenerative disorders that may have glial or neuronal inclusions made of tau, a microtubule-binding protein.
The histopathological material used for the study was derived from twenty-two human autopsy brains from patients with tauopathies. Pathological tau in neurons form neurofibrillary tangles (NFT). The sections were digitized and uploaded to an informatics platform at Mount Sinai’s Center for Computational and Systems pathology. Over 80 million tests a year are processed at Mount Sinai’s Department of Pathology, one of the largest academic pathology departments in the country with 62 full time pathologists and 900 histologists and laboratory technicians.
The convolutional network system was trained by the digitized images. The team deployed modified version of the fully convolutional SegNet architecture for the deep convolutional neural network generation, and used stochastic gradient descent for the differential loss function.