Use of Machine Learning and Gaze Tracking to Predict Radiologists’ Decisions in Breast Cancer Detection

Breast cancer is the most common cancer for women worldwide. In 2020 the GLOBOCAN estimated that 2,261,419 new breast cancer cases were diagnosed around the world, which corresponds to 11.7% of all cancers diagnosed. Moreover, incidence of this disease has been slowly rising in the US by about 0.5% per year since the mid-2000s. The most commonly used imaging modality to screen for breast cancer is digital mammography, but it has low sensitivity (particularly in dense breasts) and a relative high number of False Positives. Perhaps because of these, historically there has been much interest in developing computer-assisted tools to aid radiologists in the task of detecting early cancerous lesions. In 2022, breast imaging is a major area of interest for the developers of Artificial Intelligence (AI), and applications to detect breast cancer account for 14% of all AI applications on the medical imaging market.
In my research I have taken a different approach to the path traditionally followed by AI applications. Instead of looking at the image to detect cancer, I decided to analyze the radiologist that is reading the image, and predict the radiologist’s decisions in areas where he/she marks the location of a cancerous lesion (True and False Positives), and in areas that are fixated but do not elicit a mark (True and False Negatives). To carry out these analyses, I used eye position recording to determine what areas of the image attracted visual attention. I have shown that radiologists are consistent in the errors that they make (FPs and FNs), and that a machine learning classifier can predict these errors with good accuracy. Recently we have developed a system that not only predicts the radiologist’s decisions but also offers feedback, seeking to help to correct the errors.