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Deep Learning Models Take Shortcuts to Make Inaccurate or Biased Predictions for Cancer Patients Reviewed by Laura ThomsonJul 23 2021 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. However, unless these algorithms are properly calibrated, they can sometimes make inaccurate or biased predictions. 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 pa

Alexander pearsonLaura thomsonjulNature communicationsUniversity of chicagoAssistant professorCancer genome atlasஅலெக்சாண்டர் பியர்சன்இயற்கை தகவல்தொடர்புகள்பல்கலைக்கழகம் ஆஃப் சிகாகோஉதவியாளர் ப்ரொஃபெஸர்புற்றுநோய் மரபணு அட்லஸ்