Fig. 1
From: Deep learning predicts hip fracture using confounding patient and healthcare variables
The main source of variation in whole radiographs is explained by the device used to capture the radiograph. a Schematic of the inception v-3 deep learning model used to featurize radiographs into an embedded 2048-dimensional representation. Inception model architecture schematic derived from https://cloud.google.com/tpu/docs/inception-v3-advanced. b Data were collected from two sources. Variables were categorized as pathology (gold), image (IMG, yellow), patient, (PT, pink), or hospital process (HP, green). Italicized variables are not known at the time of image acquisition and are not used as explanatory variables. c The distribution of radiographs projected into clusters by t-Distributed Stochastic Neighbor Embedding (t-SNE) and designates how the unsupervised distribution of clusters relates to hip fracture and categorical variables
