- Keywords: Active learning, Meta-learning, Selective labeling, Chest X-ray interpretation
- TL;DR: Selective Labeling for Medical Image Classification Using Meta-Learning
- Abstract: We propose a selective labeling method using meta-learning for medical image interpretation in the setting of limited labeling resources. Our method, MedSelect, consists of a trainable deep learning model that uses image embeddings to select images to label, and a non-parametric classifier that uses cosine similarity to classify unseen images. We demonstrate that MedSelect learns an effective selection strategy outperforming baseline selection strategies across seen and unseen medical conditions for chest X-ray interpretation. We also perform an analysis of the selections performed by MedSelect comparing the distribution of latent embeddings and clinical features, and find significant differences compared to the strongest performing baseline. Our method is broadly applicable across medical imaging tasks where labels are expensive to acquire.
- Registration: I acknowledge that publication of this at MIDL and in the proceedings requires at least one of the authors to register and present the work during the conference.
- Authorship: I confirm that I am the author of this work and that it has not been submitted to another publication before.
- Paper Type: both
- Primary Subject Area: Active Learning
- Secondary Subject Area: Meta Learning
- Confidentiality And Author Instructions: I read the call for papers and author instructions. I acknowledge that exceeding the page limit and/or altering the latex template can result in desk rejection.
- Code And Data: https://github.com/stanfordmlgroup/MedSelect