Lesion Search with Self-supervised Learning
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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Methods
1x1 Convolution •
Average Pooling •
Batch Normalization •
Bottleneck Residual Block •
ColorJitter •
Contrastive Learning •
Convolution •
Dense Connections •
Feedforward Network •
Global Average Pooling •
Kaiming Initialization •
Max Pooling •
NT-Xent •
Random Gaussian Blur •
Random Resized Crop •
ReLU •
Residual Block •
Residual Connection •
ResNet •
SimCLR
