Honglak Lee

Abstract Frontier artificial intelligence (AI) models have advanced rapidly through training on internet-scale public data, yet such systems lack access to private clinical data. Neuroimaging is underrepresented in the public domain due to identifiable facial features within magnetic resonance imaging (MRI) and computed tomography (CT) scans, restricting model performance in clinical medicine.

ML: Classification and Regression, CV: Adversarial Attacks & Robustness, CV: Applications, CV: Learning & Optimization for CV, CV: Low Level & Physics-based Vision, CV: Other Foundations of Computer Vision, CV: Representation Learning for Vision, ML: Adversarial Learning & Robustness, ML: Other Foundations of Machine Learning, ML: Privacy

[Submitted on 14 Dec 2022 ( v1 ), last revised 11 Oct 2024 (this version, v3)] Title:Significantly improving zero-shot X-ray pathology classification via fine-tuning pre-trained image-text encoders View a PDF of the paper titled Significantly improving zero-shot X-ray pathology classification via fine-tuning pre-trained image-text encoders, by Jongseong Jang and 5 other authors View PDF Abstract:Deep neural networks are increasingly used in medical imaging for tasks such as pathological...