papers

Publications (6)

eess.IV2021

TorchXRayVision: A library of chest X-ray datasets and models

Joseph Paul Cohen, Joseph D. Viviano, Paul Bertin +8

TorchXRayVision is an open source software library for working with chest X-ray datasets and deep learning models. It provides a common interface and common pre-processing chain fo…

cs.CV2025

LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces

Rashid Mushkani, Shravan Nayak, Hugo Berard +3

We introduce the Local Intersectional Visual Spaces (LIVS) dataset, a benchmark for multi-criteria alignment, developed through a two-year participatory process with 30 community o…

eess.IV2020

On the limits of cross-domain generalization in automated X-ray prediction

Joseph Paul Cohen, Mohammad Hashir, Rupert Brooks +1

This large scale study focuses on quantifying what X-rays diagnostic prediction tasks generalize well across multiple different datasets. We present evidence that the issue of gene…

eess.IV2020

Quantifying the Value of Lateral Views in Deep Learning for Chest X-rays

Mohammad Hashir, Hadrien Bertrand, Joseph Paul Cohen

Most deep learning models in chest X-ray prediction utilize the posteroanterior (PA) view due to the lack of other views available. PadChest is a large-scale chest X-ray dataset th…

cs.LG2017

Classification of MRI data using Deep Learning and Gaussian Process-based Model Selection

Hadrien Bertrand, Matthieu Perrot, Roberto Ardon +1

The classification of MRI images according to the anatomical field of view is a necessary task to solve when faced with the increasing quantity of medical images. In parallel, adva…

cs.CV2019

Do Lateral Views Help Automated Chest X-ray Predictions?

Hadrien Bertrand, Mohammad Hashir, Joseph Paul Cohen

Most convolutional neural networks in chest radiology use only the frontal posteroanterior (PA) view to make a prediction. However the lateral view is known to help the diagnosis o…