Learning Generic Lung Ultrasound Biomarkers for Decoupling Feature Extraction from Downstream Tasks

• URL: http://arxiv.org/abs/2206.08398v1
• Date: Thu, 16 Jun 2022 18:15:14 GMT
• Title: Learning Generic Lung Ultrasound Biomarkers for Decoupling Feature Extraction from Downstream Tasks
• Authors: Gautam Rajendrakumar Gare, Tom Fox, Pete Lowery, Kevin Zamora, Hai V. Tran, Laura Hutchins, David Montgomery, Amita Krishnan, Deva Kannan Ramanan, Ricardo Luis Rodriguez, Bennett P deBoisblanc, John Michael Galeotti
• Abstract summary: We propose to decouple feature learning from downstream lung ultrasound tasks by introducing an auxiliary pre-task of visual biomarker classification. We demonstrate that one can learn an informative, concise, and interpretable feature space from ultrasound videos by training models for predicting biomarker labels.
• Score: 0.032270246323516584
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contemporary artificial neural networks (ANN) are trained end-to-end, jointly learning both features and classifiers for the task of interest. Though enormously effective, this paradigm imposes significant costs in assembling annotated task-specific datasets and training large-scale networks. We propose to decouple feature learning from downstream lung ultrasound tasks by introducing an auxiliary pre-task of visual biomarker classification. We demonstrate that one can learn an informative, concise, and interpretable feature space from ultrasound videos by training models for predicting biomarker labels. Notably, biomarker feature extractors can be trained from data annotated with weak video-scale supervision. These features can be used by a variety of downstream Expert models targeted for diverse clinical tasks (Diagnosis, lung severity, S/F ratio). Crucially, task-specific expert models are comparable in accuracy to end-to-end models directly trained for such target tasks, while being significantly lower cost to train.

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