1. A collaborative computer aided diagnosis (C-CAD) system with eye-tracking, sparse attentional model, and deep learning.
- Author
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Khosravan, Naji, Celik, Haydar, Turkbey, Baris, Jones, Elizabeth C., Wood, Bradford, and Bagci, Ulas
- Subjects
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COMPUTER-aided design , *EYE tracking , *PROSTATE cancer , *IMAGE analysis , *RADIOLOGISTS , *DEEP learning - Abstract
Highlights • We introduce a paradigm shift CAD system, called collaborative CAD (C-CAD), that unifies both CAD and eye-tracking to improve radiographical image analysis. • We develop an eye-tracking interface that provides a real radiology reading room experience and perform an attention based clustering and sparsification of dense eye-tracking data. • We propose a new attention based data sparsification method applied to gaze patterns of radiologists which allows local and global analysis of visual search patterns based on visual attention concepts. • By utilizing gaze patterns, we build a new CAD system based on a 3D deep learning algorithm in a newly designed multi-task learning platform where both segmentation and diagnosis tasks are jointly modeled. • We show the efficacy of the system in lung cancer screening experiments with low dose CT, and then we extend the proposed eye-tracking based CAD system into a multi-modality image analysis framework where users can utilize multiple screens as in prostate screening with multi-parametric MRI. Abstract Computer aided diagnosis (CAD) tools help radiologists to reduce diagnostic errors such as missing tumors and misdiagnosis. Vision researchers have been analyzing behaviors of radiologists during screening to understand how and why they miss tumors or misdiagnose. In this regard, eye-trackers have been instrumental in understanding visual search processes of radiologists. However, most relevant studies in this aspect are not compatible with realistic radiology reading rooms. In this study, we aim to develop a paradigm shifting CAD system, called collaborative CAD (C-CAD), that unifies CAD and eye-tracking systems in realistic radiology room settings. We first developed an eye-tracking interface providing radiologists with a real radiology reading room experience. Second, we propose a novel algorithm that unifies eye-tracking data and a CAD system. Specifically, we present a new graph based clustering and sparsification algorithm to transform eye-tracking data (gaze) into a graph model to interpret gaze patterns quantitatively and qualitatively. The proposed C-CAD collaborates with radiologists via eye-tracking technology and helps them to improve their diagnostic decisions. The C-CAD uses radiologists' search efficiency by processing their gaze patterns. Furthermore, the C-CAD incorporates a deep learning algorithm in a newly designed multi-task learning platform to segment and diagnose suspicious areas simultaneously. The proposed C-CAD system has been tested in a lung cancer screening experiment with multiple radiologists, reading low dose chest CTs. Promising results support the efficiency, accuracy and applicability of the proposed C-CAD system in a real radiology room setting. We have also shown that our framework is generalizable to more complex applications such as prostate cancer screening with multi-parametric magnetic resonance imaging (mp-MRI). [ABSTRACT FROM AUTHOR]
- Published
- 2019
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