Cross-Modal Data Programming Enables Rapid Medical Machine Learning

Author:

Dunnmon Jared A.ORCID,Ratner Alexander J.,Saab Khaled,Khandwala Nishith,Markert Matthew,Sagreiya Hersh,Goldman Roger,Lee-Messer Christopher,Lungren Matthew P.,Rubin Daniel L.ORCID,Ré Christopher

Funder

NEC Corporation

Toshiba International Foundation

National Science Foundation

Taiwan Semiconductor Manufacturing Company

Hitachi

BASF

Accenture

Qualcomm

Office of Naval Research

Intel Corporation

Google

Microsoft

National Institutes of Health

Defense Advanced Research Projects Agency

Stanford Maternal and Child Health Research Institute

Intelligence Community Postdoctoral Research Fellowship Program

Gordon and Betty Moore Foundation

Xilinx

Analog Devices

Okawa Foundation for Information and Telecommunications

Facebook

U.S. National Library of Medicine

Radiological Society of North America

Publisher

Elsevier BV

Subject

General Decision Sciences

Cited by 33 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Foundations & Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions;ACM Computing Surveys;2024-06-22

2. Automating Weak Label Generation for Data Programming with Clinicians in the Loop;2024 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE);2024-06-19

3. Curating Naturally Adversarial Datasets for Learning-Enabled Medical Cyber-Physical Systems;2024 ACM/IEEE 15th International Conference on Cyber-Physical Systems (ICCPS);2024-05-13

4. Language Models in the Loop: Incorporating Prompting into Weak Supervision;ACM / IMS Journal of Data Science;2024-04-08

5. MITER: Medical Image–TExt joint adaptive pretRaining with multi-level contrastive learning;Expert Systems with Applications;2024-03

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