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Subject [ICCVW 2019] Building a Breast-Sentence Dataset: Its Usefulness for Computer-Aided Diagnosis (by Hyebin Lee) is accepted in ICCVW 2019
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Date 2019-09-05
Title: Building a Breast-Sentence Dataset: Its Usefulness for Computer-Aided Diagnosis
Authors: Hyebin Lee, Seong Tae Kim, and Yong Man Ro

Abstract: In recent years, it is verified that the deep learning network is able to process not only images but also time-series information. Since breast image analysis plays a big role in the diagnosis of breast cancer, there have been a large number of attempts to apply the deep learning method for an accurate diagnosis. With the advance of deep learning approaches, the possibility of using medical reports (in natural language) has been increased. However, there is no public medical report dataset associated with the breast image. Instead, in the conventional public breast mammography datasets, the characteristics of breast cancer are annotated according to the standardized term (Breast Imaging-Reporting and Data System). In this study, a breast sentence dataset is proposed to investigate the usefulness of the breast-sentence dataset in computer-aided diagnosis. Based on the conventional breast mammography datasets, we annotated sentences in the natural language according to the standardized terms (defined in Breast Imaging-Reporting and Data System) in conventional breast mammography datasets. In the experiments, we show three use cases to verify the usefulness of the breast-sentence dataset: 1) CAD framework with radiologist’s input, 2) the use of sentence dataset in training a CAD, and 3) visual pointing guided by sentence.