Being Similar is Not Enough: How to Bridge Usability Gap through Diversity in Medical Images

In this paper we present a technique developed to bridge the usability gap in Content-Based Medical Image Retrieval (CBMIR) systems exploring both similarity and diversity. Usability gaps are related to how easy to use a software tool from the radiologist's perspective is. Although much have been done to better express similarity queries, the use of CBMIR over massive databases may have drawbacks that impact its usability. We claim that much of the problems derives from the fact that many images returned are closer to each other than to the query element (near-duplicates). To target this nuisance, we propose to boost similarity queries with diversity, using a technique to hierarchically cluster near-duplicates. We tailored a domain-independent and parameter-free method by controlling the maximum area reached in the search space. This novel approach to improve CBMIR systems take advantage of diversity expectations. The proposed approach BridGE (Better result with influence diversification to Group Elements) aims at adding new relevant information to the analysts, reducing the need of further query refinement or relevance feedback cycles. The results are displayed to the specialist as a traditional CBMIR result whereas the radiologists are able to expand the clusters and navigate through them. The results support our claim that a CBMIR system empowered with diversity is able to bridge the usability gap, grouping near-duplicates and being at least 2 orders of magnitude faster than its mainly competitors.