Lesion Search with Self-supervised Learning
Kristin Qi Jiali Cheng Daniel Haehn
Content-based image retrieval (CBIR) with self-supervised learning (SSL)
accelerates clinicians' interpretation of similar images without manual
annotations. We develop a CBIR from the contrastive learning SimCLR and
incorporate a generalized-mean (GeM) pooling followed by L2 normalization to
classify lesion types and retrieve similar images before clinicians' analysis.
Results have shown improved performance. We additionally build an open-source
application for image analysis and retrieval. The application is easy to
integrate, relieving manual efforts and suggesting the potential to support
clinicians' everyday activities.