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Proceedings Paper

Toward translational incremental similarity-based reasoning in breast cancer grading
Author(s): Adina E. Tutac; Daniel Racoceanu; Wee-Keng Leow; Henning Müller; Thomas Putti; Vladimir Cretu
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Paper Abstract

One of the fundamental issues in bridging the gap between the proliferation of Content-Based Image Retrieval (CBIR) systems in the scientific literature and the deficiency of their usage in medical community is based on the characteristic of CBIR to access information by images or/and text only. Yet, the way physicians are reasoning about patients leads intuitively to a case representation. Hence, a proper solution to overcome this gap is to consider a CBIR approach inspired by Case-Based Reasoning (CBR), which naturally introduces medical knowledge structured by cases. Moreover, in a CBR system, the knowledge is incrementally added and learned. The purpose of this study is to initiate a translational solution from CBIR algorithms to clinical practice, using a CBIR/CBR hybrid approach. Therefore, we advance the idea of a translational incremental similarity-based reasoning (TISBR), using combined CBIR and CBR characteristics: incremental learning of medical knowledge, medical case-based structure of the knowledge (CBR), image usage to retrieve similar cases (CBIR), similarity concept (central for both paradigms). For this purpose, three major axes are explored: the indexing, the cases retrieval and the search refinement, applied to Breast Cancer Grading (BCG), a powerful breast cancer prognosis exam. The effectiveness of this strategy is currently evaluated over cases provided by the Pathology Department of Singapore National University Hospital, for the indexing. With its current accuracy, TISBR launches interesting perspectives for complex reasoning in future medical research, opening the way to a better knowledge traceability and a better acceptance rate of computer-aided diagnosis assistance among practitioners.

Paper Details

Date Published: 27 February 2009
PDF: 12 pages
Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72603C (27 February 2009); doi: 10.1117/12.813731
Show Author Affiliations
Adina E. Tutac, Image Perception, Access and Language IPAL (Singapore)
Politehnica Univ. Timisoara (Romania)
Daniel Racoceanu, Image Perception, Access and Language IPAL (Singapore)
Univ. of Besancon (France)
Wee-Keng Leow, Image Perception, Access and Language IPAL (Singapore)
National Univ. of Singapore (Singapore)
Henning Müller, Univ. Hospital of Geneva (Switzerland)
Univ. of Applied Sciences, Western Switzerland (Switzerland)
Thomas Putti, National Univ. Hospital (Singapore)
Vladimir Cretu, Politehnica Univ. Timisoara (Romania)


Published in SPIE Proceedings Vol. 7260:
Medical Imaging 2009: Computer-Aided Diagnosis
Nico Karssemeijer; Maryellen L. Giger, Editor(s)

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