Experiences from the ImageCLEF Medical Retrieval and Annotation Tasks

The medical tasks in ImageCLEF have been run every year from 2004-2018 and many different tasks and data sets have been used over these years. The created resources are being used by many researchers well beyond the actual evaluation campaigns and are allowing to compare the performance of many techniques on the same grounds and in a reproducible way. Many of the larger data sets are from the medical literature, as such images are easier to obtain and to share than clinical data, which was used in a few smaller ImageCLEF challenges that are specifically marked with the disease type and anatomic region. This chapter describes the main results of the various tasks over the years, including data, participants, types of tasks evaluated and also the lessons learned in organizing such tasks for the scientific community

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) model (Lewis and Stevens, 1991; Lewis et al., 1994). The modelling is done by letting the predictor variables for the øth value in the time series fy ø g be given by y ø \Gamma1 (= x ø;1 ); y ø \Gamma2 (= x ø;2 ); : : : ; y ø \Gammap (= x ø;p ). Note that if we combined these predictors to form a linear additive function we would just be modelling the time series as a usual AR(p) process. However, the ASTAR method involves modelling these lagged predictors variables using a MARS model. Thus the predictor 5.6. MODELLING TIME SERIES USING BAYESIAN MARS 127 variables can have both threshold terms, because of the form of the truncated linear spline basis functions, and interaction