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Machine learning (ML) has great potential for early diagnosis of disease from medical scans, and at times, has even been shown to outperform experts. However, ML algorithms need large amounts of annotated data – scans with outlined abnormalities - for good performance. The time-consuming annotation process limits the progress of ML in this field. To address the annotation problem, multiple instance learning (MIL) algorithms were proposed, which learn from scans that have been diagnosed, but not annotated in detail. Unfortunately, these algorithms are not good enough at predicting where the abnormalities are located, which is important for diagnosis and prognosis of disease. This limits the application of these algorithms in research and in clinical practice. I propose to use the “wisdom of the crowds” –internet users without specific expertise – to improve the predictions of the algorithms. While the crowd does not have experience with medical imaging, recent studies and pilot data I collected show they can still provide useful information about the images, for example by saying whether images are visually similar or not. Such information has not been leveraged before in medical imaging applications. I will validate these methods on three challenging detection tasks in chest computed tomography, histopathology images, and endoscopy video. Understanding how the crowd can contribute to applications that typically require expert knowledge will allow harnessing the potential of large unannotated sets of data, training more reliable algorithms, and ultimately paving the way towards using ML algorithms in clinical practice.

CrowdDetective: Wisdom of the Crowds for Detecting Abnormalities in Medical Scans