Keywords: Data Processing, Software Tools
Motivation: The lack of standardization in MRI metadata increases radiologist workload.
Goal(s): To demonstrate an approach to standardize the image contrast and the body part examined information in the DICOM header and to understand the benefits of self-supervised pretraining on the metadata standardization task in the presence of label noise.
Approach: Masked language modeling was used for pretraining. At the fine-tuning stage, a transformer model was used to predict the image contrast and body part from both the text and numerical DICOM tags.
Results: Pretraining improves robustness to label noise, with there being no loss in performance at 20% label noise.
Impact: Pretraining using masked language modeling is effective at rendering a metadata stanardization system robust to label noise. Such a system can be used to standardize MRI metadata and therefore reduce radiologist workload. Future work should investigate class conditional label noise.
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