Classification of emphysema patterns is believed to be useful for improved diagnosis and prognosis of chronic obstructive pulmonary disease. Emphysema patterns can be assessed visually on lung CT scans. Visual assessment is a complex and time-consuming task performed by experts, making it unsuitable for obtaining large amounts of labeled data. We investigate if visual assessment of emphysema can be framed as an image similarity task that does not require expert. Substituting untrained annotators for experts makes it possible to label data sets much faster and at a lower cost. We use crowd annotators to gather similarity triplets and use t-distributed stochastic triplet embedding to learn an embedding. The quality of the embedding is evaluated by predicting expert assessed emphysema patterns. We find that although performance varies due to low quality triplets and randomness in the embedding, we still achieve a median F1 score of 0.58 for prediction of four patterns.

Additional Metadata
Keywords Crowdsourcing, Emphysema, Similarity learning
Persistent URL dx.doi.org/10.1007/978-3-319-67534-3_14, hdl.handle.net/1765/102133
Series Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Note e-Book not purchased by EUR
Citation
Ørting, S.N. (Silas Nyboe), Cheplygina, V, Petersen, J, Thomsen, L.H, Wille, M.M.W, & de Bruijne, M. (2017). Crowdsourced Emphysema Assessment. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). doi:10.1007/978-3-319-67534-3_14