960 citations · 2.7k across the 41 of their papers we have counts for
18 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…
GradMask: Reduce Overfitting by Regularizing Saliency
Becks Simpson, Francis Dutil, Yoshua Bengio +1
With too few samples or too many model parameters, overfitting can inhibit the ability to generalise predictions to new data. Within medical imaging, this can occur when features a…
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…
Chester: A Web Delivered Locally Computed Chest X-Ray Disease Prediction System
Joseph Paul Cohen, Paul Bertin, Vincent Frappier
In order to bridge the gap between Deep Learning researchers and medical professionals we develop a very accessible free prototype system which can be used by medical professionals…
Distribution Matching Losses Can Hallucinate Features in Medical Image Translation
Joseph Paul Cohen, Margaux Luck, Sina Honari
This paper discusses how distribution matching losses, such as those used in CycleGAN, when used to synthesize medical images can lead to mis-diagnosis of medical conditions. It se…
Count-ception: Counting by Fully Convolutional Redundant Counting
Joseph Paul Cohen, Genevieve Boucher, Craig A. Glastonbury +2
Counting objects in digital images is a process that should be replaced by machines. This tedious task is time consuming and prone to errors due to fatigue of human annotators. The…
Rapid building detection using machine learning
Joseph Paul Cohen, Wei Ding, Caitlin Kuhlman +2
This work describes algorithms for performing discrete object detection, specifically in the case of buildings, where usually only low quality RGB-only geospatial reflective imager…
RandomOut: Using a convolutional gradient norm to rescue convolutional filters
Joseph Paul Cohen, Henry Z. Lo, Wei Ding
Filters in convolutional neural networks are sensitive to their initialization. The random numbers used to initialize filters are a bias and determine if you will "win" and converg…
Crater Detection via Convolutional Neural Networks
Joseph Paul Cohen, Henry Z. Lo, Tingting Lu +1
Craters are among the most studied geomorphic features in the Solar System because they yield important information about the past and present geological processes and provide info…