Towards Semantic Visual Features for Malignancy Description within Medical Images

Semantic gap, which is the difference between low-level image features and their high-level semantics, has become very popular and witnessed great interest in the last two decades. This paper deals with this problem and proposes a hybrid approach to learn image semantic concepts for modeling visual features in discriminative learning stage. It combines the advantages of human-in-the-loop and discriminative semantic models. Herein, we investigate the expert-domain knowledge and expertise owing to expert-in-the-loop to determine medical-knowledge informations. Semantic models aim to learn the correlations between low-level features and textual words to describe malignancy signs in terms of semantic visual descriptors. These descriptors are automatically generated from low-level image features by exploiting the semantic concepts-based clinician medical-knowledge. Reported results over mammography image analysis society (MIAS) database prove the effectiveness of this work and its outperformance relative to compared approaches.