Lesion Search with Self-supervised Learning

Kristin Qi, Jiali Cheng, Daniel Haehn

Keywords: Self-supervised Learning, Open-source Tool, CBIR, Lesion Image Analysis, Frangi Filter

Abstract: 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.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2311.11014/code)