Special Report Lays Out Best Practices for Handling Bias in Radiology AI (IMAGE)
Caption
Example of how improper feature removal from imaging data may lead to bias. (A) Chest radiograph in a male patient with pneumonia. (B) Segmentation mask for the lung, generated using a deep learning model. (C) Chest radiograph is cropped based on the segmentation mask. If the cropped chest radiograph is fed to a subsequent classifier for detecting consolidations, the consolidation that is located behind the heart will be missed (arrow, A). This occurs because primary feature removal using the segmentation model was not valid and unnecessarily removed the portion of the lung located behind the heart.
Credit
Radiological Society of North America
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