Ameneh Asgari-Targhi (Brigham and Women's Hospital, USA)
Tina Kapur (Brigham and Women's Hospital, USA)
Project Description
Crowdsourced labels on medical imaging data can bridge the gap in labeled data required for training AI models for clinical applications. Using crowdsourced labels on lung ultrasound videos that have been shown (Duggan 2023) to have comparable accuracy to medical experts, we will train a deep learning model using an approach (Lucassen 2023) shown to work with expert-created labels and use the previous model performance to compare the new model's performance.
Objective
Using crowdsourced labels, train a deep learning model to classify lung ultrasound videos as having B-lines or having no B-lines.
Approach and Plan
Run model training and evaluation code from (Lucassen 2023) on crowdsourced labels
Compare model performance to same model (Lucassen 2023) trained on expert-created labels.
Draft Status
Ready - team will start page creating immediately
Category
Segmentation / Classification / Landmarking
Presenter Location
In-person
Key Investigators
Project Description
Crowdsourced labels on medical imaging data can bridge the gap in labeled data required for training AI models for clinical applications. Using crowdsourced labels on lung ultrasound videos that have been shown (Duggan 2023) to have comparable accuracy to medical experts, we will train a deep learning model using an approach (Lucassen 2023) shown to work with expert-created labels and use the previous model performance to compare the new model's performance.
Objective
Approach and Plan
Progress and Next Steps
Illustrations
No response
Background and References
No response