Using real multi-view radiographs from the same study as self-supervised training pairs yields better anatomical representations and downstream veterinary task performance than synthetic single-image augmentations.
Do Lateral Views Help Automated Chest X-ray Predictions?
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abstract
Most convolutional neural networks in chest radiology use only the frontal posteroanterior (PA) view to make a prediction. However the lateral view is known to help the diagnosis of certain diseases and conditions. The recently released PadChest dataset contains paired PA and lateral views, allowing us to study for which diseases and conditions the performance of a neural network improves when provided a lateral x-ray view as opposed to a frontal posteroanterior (PA) view. Using a simple DenseNet model, we find that using the lateral view increases the AUC of 8 of the 56 labels in our data and achieves the same performance as the PA view for 21 of the labels. We find that using the PA and lateral views jointly doesn't trivially lead to an increase in performance but suggest further investigation.
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VET-DINO: Learning Anatomical Understanding Through Multi-View Distillation in Veterinary Imaging
Using real multi-view radiographs from the same study as self-supervised training pairs yields better anatomical representations and downstream veterinary task performance than synthetic single-image augmentations.