  The global challenge in chest radiograph X-ray (CXR) abnormalities often
being misdiagnosed is primarily associated with perceptual errors, where
healthcare providers struggle to accurately identify the location of
abnormalities, rather than misclassification errors. We currently address this
problem through disease-specific segmentation models. Unfortunately, these
models cannot be released in the field due to their lack of generalizability
across all thoracic diseases. A binary model tends to perform poorly when it
encounters a disease that isn't represented in the dataset. We present
CheX-nomaly: a binary localization U-net model that leverages transfer learning
techniques with the incorporation of an innovative contrastive learning
approach. Trained on the VinDr-CXR dataset, which encompasses 14 distinct
diseases in addition to 'no finding' cases, my model achieves generalizability
across these 14 diseases and others it has not seen before. We show that we can
significantly improve the generalizability of an abnormality localization model
by incorporating a contrastive learning method and dissociating the bounding
boxes with its disease class. We also introduce a new loss technique to apply
to enhance the U-nets performance on bounding box segmentation. By introducing
CheX-nomaly, we offer a promising solution to enhance the precision of chest
disease diagnosis, with a specific focus on reducing the significant number of
perceptual errors in healthcare.
