185 citations · 565 across the 17 of their papers we have counts for
18 papers
Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models
Siyi Tang, Jared A. Dunnmon, Liangqiong Qu +4
Multivariate biosignals are prevalent in many medical domains, such as electroencephalography, polysomnography, and electrocardiography. Modeling spatiotemporal dependencies in mul…
xView3-SAR: Detecting Dark Fishing Activity Using Synthetic Aperture Radar Imagery
Fernando Paolo, Tsu-ting Tim Lin, Ritwik Gupta +5
Unsustainable fishing practices worldwide pose a major threat to marine resources and ecosystems. Identifying vessels that do not show up in conventional monitoring systems -- know…
Multimodal spatiotemporal graph neural networks for improved prediction of 30-day all-cause hospital readmission
Siyi Tang, Amara Tariq, Jared Dunnmon +5
Measures to predict 30-day readmission are considered an important quality factor for hospitals as accurate predictions can reduce the overall cost of care by identifying high risk…
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…
OncoNet: Weakly Supervised Siamese Network to automate cancer treatment response assessment between longitudinal FDG PET/CT examinations
Anirudh Joshi, Sabri Eyuboglu, Shih-Cheng Huang +5
FDG PET/CT imaging is a resource intensive examination critical for managing malignant disease and is particularly important for longitudinal assessment during therapy. Approaches…
Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis
Siyi Tang, Jared A. Dunnmon, Khaled Saab +5
Automated seizure detection and classification from electroencephalography (EEG) can greatly improve seizure diagnosis and treatment. However, several modeling challenges remain un…
No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems
Nimit S. Sohoni, Jared A. Dunnmon, Geoffrey Angus +2
In real-world classification tasks, each class often comprises multiple finer-grained "subclasses." As the subclass labels are frequently unavailable, models trained using only the…
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.…
Assessing Robustness to Noise: Low-Cost Head CT Triage
Sarah M. Hooper, Jared A. Dunnmon, Matthew P. Lungren +4
Automated medical image classification with convolutional neural networks (CNNs) has great potential to impact healthcare, particularly in resource-constrained healthcare systems w…
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…
Predicting US State-Level Agricultural Sentiment as a Measure of Food Security with Tweets from Farming Communities
Jared Dunnmon, Swetava Ganguli, Darren Hau +1
The ability to obtain accurate food security metrics in developing areas where relevant data can be sparse is critically important for policy makers tasked with implementing food a…
Predicting Food Security Outcomes Using Convolutional Neural Networks (CNNs) for Satellite Tasking
Swetava Ganguli, Jared Dunnmon, Darren Hau
Obtaining reliable data describing local Food Security Metrics (FSM) at a granularity that is informative to policy-makers requires expensive and logistically difficult surveys, pa…
Training Complex Models with Multi-Task Weak Supervision
Alexander Ratner, Braden Hancock, Jared Dunnmon +3
As machine learning models continue to increase in complexity, collecting large hand-labeled training sets has become one of the biggest roadblocks in practice. Instead, weaker for…
Learning to Compose Domain-Specific Transformations for Data Augmentation
Alexander J. Ratner, Henry R. Ehrenberg, Zeshan Hussain +2
Data augmentation is a ubiquitous technique for increasing the size of labeled training sets by leveraging task-specific data transformations that preserve class labels. While it i…
Optimizing and Visualizing Deep Learning for Benign/Malignant Classification in Breast Tumors
Darvin Yi, Rebecca Lynn Sawyer, David Cohn +4
Breast cancer has the highest incidence and second highest mortality rate for women in the US. Our study aims to utilize deep learning for benign/malignant classification of mammog…
Machine Learning for Better Models for Predicting Bond Prices
Swetava Ganguli, Jared Dunnmon
Bond prices are a reflection of extremely complex market interactions and policies, making prediction of future prices difficult. This task becomes even more challenging due to the…