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.