49 citations · 121 across the 6 of their papers we have counts for
5 papers · 1 filter
Domino: Discovering Systematic Errors with Cross-Modal Embeddings
Sabri Eyuboglu, Maya Varma, Khaled Saab +5
Machine learning models that achieve high overall accuracy often make systematic errors on important subsets (or slices) of data. Identifying underperforming slices is particularly…
Data Valuation for Medical Imaging Using Shapley Value: Application on A Large-scale Chest X-ray Dataset
Siyi Tang, Amirata Ghorbani, Rikiya Yamashita +4
The reliability of machine learning models can be compromised when trained on low quality data. Many large-scale medical imaging datasets contain low quality labels extracted from…
Ivy: Instrumental Variable Synthesis for Causal Inference
Zhaobin Kuang, Frederic Sala, Nimit Sohoni +5
A popular way to estimate the causal effect of a variable x on y from observational data is to use an instrumental variable (IV): a third variable z that affects y only through x.…
Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging
Luke Oakden-Rayner, Jared Dunnmon, Gustavo Carneiro +1
Machine learning models for medical image analysis often suffer from poor performance on important subsets of a population that are not identified during training or testing. For e…
Cross-Modal Data Programming Enables Rapid Medical Machine Learning
Jared Dunnmon, Alexander Ratner, Nishith Khandwala +8
Labeling training datasets has become a key barrier to building medical machine learning models. One strategy is to generate training labels programmatically, for example by applyi…