960 citations · 2.7k across the 41 of their papers we have counts for
45 papers
A Vision-Language Foundation Model to Enhance Efficiency of Chest X-ray Interpretation
Zhihong Chen, Maya Varma, Justin Xu +20
Over 1.4 billion chest X-rays (CXRs) are performed annually due to their cost-effectiveness as an initial diagnostic test. This scale of radiological studies provides a significant…
Identifying Spurious Correlations using Counterfactual Alignment
Joseph Paul Cohen, Louis Blankemeier, Akshay Chaudhari
Models driven by spurious correlations often yield poor generalization performance. We propose the counterfactual (CF) alignment method to detect and quantify spurious correlations…
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…
Benefits of Linear Conditioning with Metadata for Image Segmentation
Andreanne Lemay, Charley Gros, Olivier Vincent +3
Medical images are often accompanied by metadata describing the image (vendor, acquisition parameters) and the patient (disease type or severity, demographics, genomics). This meta…
Gifsplanation via Latent Shift: A Simple Autoencoder Approach to Counterfactual Generation for Chest X-rays
Joseph Paul Cohen, Rupert Brooks, Sovann En +4
Motivation: Traditional image attribution methods struggle to satisfactorily explain predictions of neural networks. Prediction explanation is important, especially in medical imag…
ivadomed: A Medical Imaging Deep Learning Toolbox
Charley Gros, Andreanne Lemay, Olivier Vincent +4
ivadomed is an open-source Python package for designing, end-to-end training, and evaluating deep learning models applied to medical imaging data. The package includes APIs, comman…
S2SD: Simultaneous Similarity-based Self-Distillation for Deep Metric Learning
Karsten Roth, Timo Milbich, Björn Ommer +2
Deep Metric Learning (DML) provides a crucial tool for visual similarity and zero-shot applications by learning generalizing embedding spaces, although recent work in DML has shown…
A Benchmark of Medical Out of Distribution Detection
Tianshi Cao, Chin-Wei Huang, David Yu-Tung Hui +1
Motivation: Deep learning models deployed for use on medical tasks can be equipped with Out-of-Distribution Detection (OoDD) methods in order to avoid erroneous predictions. Howeve…
Uniformizing Techniques to Process CT scans with 3D CNNs for Tuberculosis Prediction
Hasib Zunair, Aimon Rahman, Nabeel Mohammed +1
A common approach to medical image analysis on volumetric data uses deep 2D convolutional neural networks (CNNs). This is largely attributed to the challenges imposed by the nature…
Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning
Joseph Paul Cohen, Lan Dao, Paul Morrison +8
Purpose: The need to streamline patient management for COVID-19 has become more pressing than ever. Chest X-rays provide a non-invasive (potentially bedside) tool to monitor the pr…
COVID-19 Image Data Collection: Prospective Predictions Are the Future
Joseph Paul Cohen, Paul Morrison, Lan Dao +3
Across the world's coronavirus disease 2019 (COVID-19) hot spots, the need to streamline patient diagnosis and management has become more pressing than ever. As one of the main ima…
DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning
Timo Milbich, Karsten Roth, Homanga Bharadhwaj +4
Visual Similarity plays an important role in many computer vision applications. Deep metric learning (DML) is a powerful framework for learning such similarities which not only gen…
COVID-19 Image Data Collection
Joseph Paul Cohen, Paul Morrison, Lan Dao
This paper describes the initial COVID-19 open image data collection. It was created by assembling medical images from websites and publications and currently contains 123 frontal…
Spine intervertebral disc labeling using a fully convolutional redundant counting model
Lucas Rouhier, Francisco Perdigon Romero, Joseph Paul Cohen +1
Labeling intervertebral discs is relevant as it notably enables clinicians to understand the relationship between a patient's symptoms (pain, paralysis) and the exact level of spin…
Automatic segmentation of spinal multiple sclerosis lesions: How to generalize across MRI contrasts?
Olivier Vincent, Charley Gros, Joseph Paul Cohen +1
Despite recent improvements in medical image segmentation, the ability to generalize across imaging contrasts remains an open issue. To tackle this challenge, we implement Feature-…
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…
Revisiting Training Strategies and Generalization Performance in Deep Metric Learning
Karsten Roth, Timo Milbich, Samarth Sinha +3
Deep Metric Learning (DML) is arguably one of the most influential lines of research for learning visual similarities with many proposed approaches every year. Although the field b…
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…
Navigation Agents for the Visually Impaired: A Sidewalk Simulator and Experiments
Martin Weiss, Simon Chamorro, Roger Girgis +7
Millions of blind and visually-impaired (BVI) people navigate urban environments every day, using smartphones for high-level path-planning and white canes or guide dogs for local i…
Icentia11K: An Unsupervised Representation Learning Dataset for Arrhythmia Subtype Discovery
Shawn Tan, Guillaume Androz, Ahmad Chamseddine +4
We release the largest public ECG dataset of continuous raw signals for representation learning containing 11 thousand patients and 2 billion labelled beats. Our goal is to enable…
The TCGA Meta-Dataset Clinical Benchmark
Mandana Samiei, Tobias Würfl, Tristan Deleu +6
Machine learning is bringing a paradigm shift to healthcare by changing the process of disease diagnosis and prognosis in clinics and hospitals. This development equips doctors and…
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…
Deep Semantic Segmentation of Natural and Medical Images: A Review
Saeid Asgari Taghanaki, Kumar Abhishek, Joseph Paul Cohen +2
The semantic image segmentation task consists of classifying each pixel of an image into an instance, where each instance corresponds to a class. This task is a part of the concept…
Saliency is a Possible Red Herring When Diagnosing Poor Generalization
Joseph D. Viviano, Becks Simpson, Francis Dutil +2
Poor generalization is one symptom of models that learn to predict target variables using spuriously-correlated image features present only in the training distribution instead of…