Search Results for author: Anuj Pareek

Found 7 papers, 3 papers with code

Gifsplanation via Latent Shift: A Simple Autoencoder Approach to Counterfactual Generation for Chest X-rays

1 code implementation18 Feb 2021 Joseph Paul Cohen, Rupert Brooks, Sovann En, Evan Zucker, Anuj Pareek, Matthew P. Lungren, Akshay Chaudhari

We also found that the Latent Shift explanation allows a user to have more confidence in true positive predictions compared to traditional approaches (0. 15$\pm$0. 95 in a 5 point scale with p=0. 01) with only a small increase in false positive predictions (0. 04$\pm$1. 06 with p=0. 57).

CheXternal: Generalization of Deep Learning Models for Chest X-ray Interpretation to Photos of Chest X-rays and External Clinical Settings

no code implementations17 Feb 2021 Pranav Rajpurkar, Anirudh Joshi, Anuj Pareek, Andrew Y. Ng, Matthew P. Lungren

Recent advances in training deep learning models have demonstrated the potential to provide accurate chest X-ray interpretation and increase access to radiology expertise.

CheXphotogenic: Generalization of Deep Learning Models for Chest X-ray Interpretation to Photos of Chest X-rays

no code implementations12 Nov 2020 Pranav Rajpurkar, Anirudh Joshi, Anuj Pareek, Jeremy Irvin, Andrew Y. Ng, Matthew Lungren

In this study, we measured the diagnostic performance for 8 different chest x-ray models when applied to photos of chest x-rays.

CheXpedition: Investigating Generalization Challenges for Translation of Chest X-Ray Algorithms to the Clinical Setting

no code implementations26 Feb 2020 Pranav Rajpurkar, Anirudh Joshi, Anuj Pareek, Phil Chen, Amirhossein Kiani, Jeremy Irvin, Andrew Y. Ng, Matthew P. Lungren

First, we find that the top 10 chest x-ray models on the CheXpert competition achieve an average AUC of 0. 851 on the task of detecting TB on two public TB datasets without fine-tuning or including the TB labels in training data.

Translation

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