Cleaning and Harmonizing Medical Image Data for Reliable AI: Lessons Learned from Longitudinal Oral Cancer Natural History Study Data.

For deep learning-based machine learning, not only are large and sufficiently diverse data crucial but their good qualities are equally important. However, in real-world applications, it is very common that raw source data may contain incorrect, noisy, inconsistent, improperly formatted and sometimes missing elements, particularly, when the datasets are large and sourced from many sites. In this paper, we present our work towards preparing and making image data ready for the development of AI-driven approaches for studying various aspects of the natural history of oral cancer.

Open World Active Learning for Echocardiography View Classification.

Existing works for automated echocardiography view classification are designed under the assumption that the views in the testing set must belong to a limited number of views that have appeared in the training set. Such a design is called closed world classification. This assumption may be too strict for real-world environments that are open and often have unseen examples, drastically weakening the robustness of the classical view classification approaches.