Lesion Search with Self-supervised Learning

Kristin Qi; Jiali Cheng; Daniel Haehn

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arXiv · 2311.11014

Lesion Search with Self-supervised Learning

Kristin Qi·Jiali Cheng·Daniel Haehn

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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.

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Kristin Qi, Jiali Cheng, Daniel Haehn. 2023-11-18. Lesion Search with Self-supervised Learning. https://arxiv.org/abs/2311.11014

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