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Fig. 3 | BMC Medical Informatics and Decision Making

Fig. 3

From: BarlowTwins-CXR: enhancing chest X-ray abnormality localization in heterogeneous data with cross-domain self-supervised learning

Fig. 3The alternative text for this image may have been generated using AI.

Schematic Overview of the Dual-phase Training Framework. The upper panel illustrates the Barlow Twins method in Phase One, where pairs of distorted images are processed through a shared ResNet50 network to produce embeddings. These are then compared using an empirical cross-correlation matrix C, striving for the identity matrix I to minimize redundancy in feature dimensions, and optimizing the loss function \(L_{BT}\). In Phase Two (lower panel), the pre-trained ResNet50 backbone from Phase One is integrated into a Faster R-CNN architecture. It starts with multi-scale feature extraction through the Feature Pyramid Network (FPN), followed by the Region Proposal Network (RPN) that generates object region proposals. The features are then pooled and processed by fully connected (FC) layers to output the final class labels and bounding box coordinates for object detection tasks

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