IBM T.J. Watson Research Center, Multimedia Analytics: Modality Classification and Case-Based Retrieval Tasks of ImageCLEF2012.
Liangliang Cao, Yuan‐Chi Chang, Noel Codella, Michele Merler, Quoc-Bao Nguyen, John R. Smith · CLEF (Online Working Notes/Labs/Workshop) · 2012
In this paper we present the modeling strategies that were applied by the IBM T.J. Watson research team to the modality classi- cation and case-based retrieval tasks of ImageCLEF 2012. The primary challenges of this year's medical modality classication task were as follows: 1) the supplied training data was extremely limited, with some categories having as few as 5 positive examples, leaving little room for internal testing, and 2) some modalities appeared to be visually similar. In order to address these challenges, we approached the task from two fronts: 1) we attempted to augment the training data with additional examples of each category, and 2) we experimented with a broad range of modeling strategies and feature extraction techniques. For the case based retrieval task, we employed a semantic similarity approach to measure the relatedness among medical concepts found in the text corpus. We believe the lack of using additional lexical database besides the UMLS-methathesaurus led to poor performance in relation to other approaches.