110 citations · 138 across the 9 of their papers we have counts for
5 papers · 1 filter
Towards generative adversarial networks as a new paradigm for radiology education
Samuel G. Finlayson, Hyunkwang Lee, Isaac S. Kohane +1
Medical students and radiology trainees typically view thousands of images in order to "train their eye" to detect the subtle visual patterns necessary for diagnosis. Nevertheless,…
Learning Contextual Hierarchical Structure of Medical Concepts with Poincairé Embeddings to Clarify Phenotypes
Brett K. Beaulieu-Jones, Isaac S. Kohane, Andrew L. Beam
Biomedical association studies are increasingly done using clinical concepts, and in particular diagnostic codes from clinical data repositories as phenotypes. Clinical concepts ca…
A Fast Divide-and-Conquer Sparse Cox Regression
Yan Wang, Nathan Palmer, Qian Di +3
We propose a computationally and statistically efficient divide-and-conquer (DAC) algorithm to fit sparse Cox regression to massive datasets where the sample size is exceedin…
Adversarial Attacks Against Medical Deep Learning Systems
Samuel G. Finlayson, Hyung Won Chung, Isaac S. Kohane +1
The discovery of adversarial examples has raised concerns about the practical deployment of deep learning systems. In this paper, we demonstrate that adversarial examples are capab…
Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data
Andrew L. Beam, Benjamin Kompa, Allen Schmaltz +6
Word embeddings are a popular approach to unsupervised learning of word relationships that are widely used in natural language processing. In this article, we present a new set of…