Sumary of Artificial intelligence models to analyze cancer images take shortcuts that introduce bias:
- Artificial intelligence tools and deep learning models are a powerful tool in cancer treatment.
- They can be used to analyze digital images of tumor biopsy samples, helping physicians quickly classify the type of cancer, predict prognosis and guide a course of treatment for the patient.
- A new study led by researchers from the University of Chicago shows that deep learning models trained on large sets of cancer genetic and tissue histology data can easily identify the institution that submitted the images.
- The models, which use machine learning methods to “teach” themselves how to recognize certain cancer signatures, end up using the submitting site as a shortcut to predicting outcomes for the patient, lumping them together with other patients from the same location instead of relying on the biology of individual patients.
- “We identified a glaring hole in the in the current methodology for deep learning model development which makes certain regions and patient populations more susceptible to be included in inaccurate algorithmic predictions,” said Alexander Pearson, MD, PhD, assistant Assistant Professor of Medicine at UChicago Medicine and co-senior author.
- Digital images can then be created for storage and remote analysis by using a scanning microscope.
- While these steps are mostly standard across pathology labs, minor variations in the color or amount of stain, tissue processing techniques and in the imaging equipment can create unique signatures, like tags, on each image.
- These location-specific signatures aren’t visible to the naked eye, but are easily detected by powerful deep learning algorithms.