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Found 14 papers, 6 papers with code
ViLMedic: a framework for research at the intersection of vision and language in medical AI
There is a growing need to model interactions between data modalities (e. g., vision, language) — both to improve AI predictions on existing tasks and to enable new applications.
xView3-SAR: Detecting Dark Fishing Activity Using Synthetic Aperture Radar Imagery
Unsustainable fishing practices worldwide pose a major threat to marine resources and ecosystems.
Ranked #1 on
Holdout Set
on xView3-SAR
Multimodal spatiotemporal graph neural networks for improved prediction of 30-day all-cause hospital readmission
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 patients before they are discharged.
Domino: Discovering Systematic Errors with Cross-Modal Embeddings
In this work, we address these challenges by first designing a principled evaluation framework that enables a quantitative comparison of SDMs across 1, 235 slice discovery settings in three input domains (natural images, medical images, and time-series data).
OncoNet: Weakly Supervised Siamese Network to automate cancer treatment response assessment between longitudinal FDG PET/CT examinations
FDG PET/CT imaging is a resource intensive examination critical for managing malignant disease and is particularly important for longitudinal assessment during therapy.
Ivy: Instrumental Variable Synthesis for Causal Inference
To relax these assumptions, we propose Ivy, a new method to combine IV candidates that can handle correlated and invalid IV candidates in a robust manner.
Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging
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.
Cross-Modal Data Programming Enables Rapid Medical Machine Learning
Labeling training datasets has become a key barrier to building medical machine learning models.
Improving Sample Complexity with Observational Supervision
Supervised machine learning models for high-value computer vision applications such as medical image classification often require large datasets labeled by domain experts, which are slow to collect, expensive to maintain, and static with respect to changes in the data distribution.
Predicting Food Security Outcomes Using Convolutional Neural Networks (CNNs) for Satellite Tasking
Obtaining reliable data describing local Food Security Metrics (FSM) at a granularity that is informative to policy-makers requires expensive and logistically difficult surveys, particularly in the developing world.
Predicting US State-Level Agricultural Sentiment as a Measure of Food Security with Tweets from Farming Communities
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 aid programs.
Training Complex Models with Multi-Task Weak Supervision
Snorkel MeTaL: A framework for training models with multi-task weak supervision
Ranked #1 on
Semantic Textual Similarity
on SentEval
Learning to Compose Domain-Specific Transformations for Data Augmentation
Data augmentation is a ubiquitous technique for increasing the size of labeled training sets by leveraging task-specific data transformations that preserve class labels.
Optimizing and Visualizing Deep Learning for Benign/Malignant Classification in Breast Tumors
Breast cancer has the highest incidence and second highest mortality rate for women in the US.
