Learning from Everyday Images Enables Expert-like Diagnosis of Retinal Diseases

Abstract

Kermany et al. report an application of a neural network trained on millions of everyday images to a database of thousands of retinal tomography images that they gathered and expert labeled, resulting in a rapid and accurate diagnosis of retinal diseases.

Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning

“…Reasons to learn features from raw data include that doing so often substantially improves performance ( 13 , 25 , 31 ); because such features can be transferred to other domains with small datasets ( 32 , 33 ); because it is time-consuming to manually design features; and because a general algorithm that learns features automatically can improve performance on very different types of data [e.g., sound ( 20 , 34 ) and text ( 23 , 35 )], increasing the impact of the approach. However, an additional benefit to deep learning is that if hand-designed features are thought to be useful, they can be included as well in case they improve performance ( 36 – 40 ).…”

Preoperative identification of microvascular invasion in hepatocellular carcinoma by XGBoost and deep learning

“…Advances in imaging technology, together with artificial intelligence (Bi et al 2019), have allowed researchers to create various diagnostic and treatment models and improved the diagnostic efficacy in liver cancer, dermatology, ophthalmology, lung and breast cancers, neurology, cardiovascular diseases, gastrointestinal endoscopy, and genetic diseases, etc. (Attia et al 2019;Chilamkurthy et al 2018;Coudray et al 2018;Esteva et al 2017;Gurovich et al 2019;Kermany et al 2018;Mori et al 2018;Rampasek and Goldenberg 2018;Yasaka et al 2018;Zou et al 2019). The purpose of the current study is to develop models using eXtreme Gradient Boosting (XGBoost) and deep learning to provide a preoperative non-invasive assessment method for MVI in HCC patients.…”

Identifying Smoking Environments From Images of Daily Life With Deep Learning

“…A type of deep learning model called the convolutional neural network (CNN) can be applied to identify objects and settings present in the image or make other image-related predictions. Typically, CNNs are initially trained with everyday images, 17,18 but they have been successfully repurposed for clinical applications including identifying diabetic retinopathy 19,20 and skin cancer. 21 Moreover, computationally efficient CNNs (ie, with fewer parameters) have now been developed for mobile devices, allowing images to be rapidly analyzed with a smartphone or other device without substantially compromising performance.…”