Quantifying the Value of Lateral Views in Deep Learning for Chest X-raysDownload PDF

Published: 18 Apr 2020, Last Modified: 03 Jul 2024MIDL 2020Readers: Everyone
Keywords: convolutional neural networks, chest x-rays, lateral views, multi-label classification
Track: full conference paper
Abstract: Most deep learning models in chest X-ray prediction utilize the posteroanterior (PA) view due to the lack of other views available. PadChest is a large-scale chest X-ray dataset that has almost 200 labels and multiple views available. In this work, we use PadChest to explore multiple approaches to merging the PA and lateral views for predicting the radiological labels associated with the X-ray image. We find that different methods of merging the model utilize the lateral view differently. We also find that including the lateral view increases performance for 32 labels in the dataset, while being neutral for the others. The increase in overall performance is comparable to the one obtained by using only the PA view with twice the amount of patients in the training set.
Paper Type: both
Source Latex: zip
Presentation Upload: zip
Presentation Upload Agreement: I agree that my presentation material (videos and slides) will be made public.
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/quantifying-the-value-of-lateral-views-in/code)
8 Replies