Breast Cancer Diagnosis through Active Learning in Content-based Image Retrieval

Publication date: Available online 17 May 2019Source: NeurocomputingAuthor(s): Rafael S. Bressan, Pedro H. Bugatti, Priscila T.M. SaitoAbstractOne of the cornerstones of content-based image retrieval (CBIR) for medical image diagnosis is to select the images that present higher similarity with a given query image. Different from previous literature efforts, the present work aims to seamlessly fuse a powerful machine learning strategy based on the active learning paradigm, in order to obtain greater efficacy regarding similarity queries in medical CBIR systems. To do so, we propose a new approach, named as Medical Active leaRning and Retrieval (MARRow) to aid the breast cancer diagnosis. It enables to deal with more feasible strategies, specifically for the medical context and its inherent constraints. We also proposed an active learning strategy to select a small set of more informative images, considering selection criteria based on not only similarity, but also on certain degrees of diversity and uncertainty. To validate our proposed approach, we performed experiments using public medical image datasets, different descriptors for each one and compared our approach against four widely applied and well-known literature approaches, such as: Traditional CBIR without relevance feedback strategies, Query Point Movement Strategy (QPM), Query Expansion (QEX) and SVM Active Learning (SVM-AL). From the experiments, we can observe that our approach presents a strong performance over s...

Source: Neurocomputing - May 17, 2019 Category: Neuroscience Source Type: research