Lesion Search with Self-supervised Learning
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 13 references that are in the map. First posted on arXiv 18 Nov 2023.
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4 of 13 references.
References · 4
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
- 2017Large Batch Training of Convolutional Networkscited by 535
- 2017Fine-tuning CNN Image Retrieval with No Human Annotationcited by 499
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