Visually interpretable deep network for diagnosis of breast masses on mammograms.

Abstract:

:Recently, deep learning technology has achieved various successes in medical image analysis studies including computer-aided diagnosis (CADx). However, current CADx approaches based on deep learning have a limitation in interpreting diagnostic decisions. The limited interpretability is a major challenge for practical use of current deep learning approaches. In this paper, a novel visually interpretable deep network framework is proposed to provide diagnostic decisions with visual interpretation. The proposed method is motivated by the fact that the radiologists characterize breast masses according to the breast imaging reporting and data system (BIRADS). The proposed deep network framework consists of a BIRADS guided diagnosis network and a BIRADS critic network. A 2D map, named BIRADS guide map, is generated in the inference process of the deep network. The visual features extracted from the breast masses could be refined by the BIRADS guide map, which helps the deep network to focus on more informative areas. The BIRADS critic network makes the BIRADS guide map to be relevant to the characterization of masses in terms of BIRADS description. To verify the proposed method, comparative experiments have been conducted on public mammogram database. On the independent test set (170 malignant masses and 170 benign masses), the proposed method was found to have significantly higher performance compared to the deep network approach without using the BIRADS guide map (p  <  0.05). Moreover, the visualization was conducted to show the location where the deep network exploited more information. This study demonstrated that the proposed visually interpretable CADx framework could be a promising approach for visually interpreting the diagnostic decision of the deep network.

journal_name

Phys Med Biol

authors

Kim ST,Lee JH,Lee H,Ro YM

doi

10.1088/1361-6560/aaef0a

subject

Has Abstract

pub_date

2018-12-04 00:00:00

pages

235025

issue

23

eissn

0031-9155

issn

1361-6560

journal_volume

63

pub_type

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