papers

Publications (7)

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…

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…

q-bio.GN2020

Analysis of Gene Interaction Graphs as Prior Knowledge for Machine Learning Models

Paul Bertin, Mohammad Hashir, Martin Weiss +4

Gene interaction graphs aim to capture various relationships between genes and can represent decades of biology research. When trying to make predictions from genomic data, those g…

q-bio.GN2019

Is graph-based feature selection of genes better than random?

Mohammad Hashir, Paul Bertin, Martin Weiss +4

Gene interaction graphs aim to capture various relationships between genes and represent decades of biology research. When trying to make predictions from genomic data, those graph…

cs.LG2019

Towards unstructured mortality prediction with free-text clinical notes

Mohammad Hashir, Rapinder Sawhney

Healthcare data continues to flourish yet a relatively small portion, mostly structured, is being utilized effectively for predicting clinical outcomes. The rich subjective informa…

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…