Скачать или смотреть MIA: Gopal, Han, Raghupathi, Contrastive learning of electrocardiograms; Primer by Pranav Rajpurkar

Models, Inference and Algorithms
Broad Institute of MIT and Harvard
October 6, 2021


Bryan Gopal
Stanford University

Ryan Han
Stanford University

Gautham Raghupathi
Stanford University

3KG: Contrastive learning of 12-lead electrocardiograms using physiologically-inspired augmentations

We propose 3KG, a physiologically-inspired contrastive learning approach that generates views using 3D augmentations of the 12-lead electrocardiogram. We evaluate representation quality by fine-tuning a linear layer for the downstream task of 23 class diagnosis on the PhysioNet 2020 challenge training data and find that 3KG achieves a 9.1% increase in mean AUC over the best self-supervised baseline when trained on 1% of labeled data. Our empirical analysis shows that combining spatial and temporal augmentations produces the strongest representations. In addition, we investigate the effect of this physiologically-inspired pretraining on downstream performance on different disease subgroups and find that 3KG makes the greatest gains for conduction and rhythm abnormalities. Our method allows for flexibility in incorporating other self-supervised strategies and highlights the potential for similar modality-specific augmentations for other biomedical signals.



Pranav Rajpurkar
Dept. of Biomedical Informatics, Harvard Medical School

Primer: Advancements and challenges for deep learning in medical imaging

There have been rapid advances at the intersection of AI and medicine over the last few years, especially for the interpretation of medical images. In this talk, I will describe three key directions that present challenges and opportunities for the development of deep learning technologies for medical image interpretation. First, I will discuss the development of transfer learning and self-supervised learning algorithms designed to work in low labeled medical data settings. Second, I will discuss the design and curation of large, high-quality datasets and their roles in advancing algorithmic developments. Third, I will discuss the real-world impact of AI technologies on clinicians’ decision making and subtleties for the promise of expert-AI collaboration. Altogether I will summarize key recent contributions and insights in each of these directions with key applications across medical specialties.

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