Exclude at your own peril: Evaluating the omission of data in training AI breast cancer risk models

Bias in AI training data can create bias in the model produced, this is of particular importance in medical scenarios to ensure fair and equitable representation. This work explores the data cleaning practices used and their effect on a model trained to predict breast density, a strong indicator of cancer risk. This work’s primary focus is the removal of patient mammograms with more than the standard four views. This can be because the breast was too large to fit in a single image, or because of a technical repeat. The results of this work show that inclusion of this data does not hinder AI training, and their data alone is sufficient to train AI. Exploring exclusion criteria is important as any systematic practice can leave particular demographics unrepresented in the data. This is an important step towards fair representation in AI and ensuring its benefits can be seen by all.