A collaborative computer aided diagnosis (C-CAD) system with eye-tracking, sparse attentional model, and deep learning.
A collaborative computer aided diagnosis (C-CAD) system with eye-tracking, sparse attentional model, and deep learning.
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协同计算机辅助诊断（C-CAD）系统，具有眼动跟踪，稀疏注意力模型和深度学习。
DOI：
10.1016/j.media.2018.10.010
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发表时间：
2019-01
影响因子：
10.9
通讯作者：
Bagci U
中科院分区：
文献类型：
--
作者：
Khosravan N;Celik H;Turkbey B;Jones EC;Wood B;Bagci U
计算机辅助诊断(CAD)工具帮助放射科医生减少诊断错误，如丢失肿瘤和误诊。视觉研究人员一直在分析放射科医生在筛查过程中的行为，以了解他们如何以及为什么会漏掉肿瘤或误诊。在这方面，眼球追踪器有助于理解放射科医生的视觉搜索过程。然而，这方面的大多数相关研究与现实的放射学阅览室不兼容。在这项研究中，我们的目标是开发一个范式转换的CAD系统，称为协作式CAD(C-CAD)，在现实的放射室环境中统一CAD和眼球跟踪系统。我们首先开发了一个眼球跟踪界面，为放射科医生提供真正的放射学阅览室体验。其次，我们提出了一种将眼球跟踪数据与CAD系统相结合的新算法。具体地说，我们提出了一种新的基于图的聚类和稀疏化算法，将眼睛跟踪数据(凝视)转换为图模型，以定量和定性地解释凝视模式。拟议的C-CAD通过眼睛跟踪技术与放射科医生合作，帮助他们改进诊断决策。C-CAD通过处理放射科医生的凝视模式来利用他们的搜索效率。此外，C-CAD在新设计的多任务学习平台中融入了深度学习算法，以同时分割和诊断可疑区域。建议的C-CAD系统已经在多名放射科医生的肺癌筛查实验中进行了测试，读取了低剂量的胸部CT。前景看好的结果支持了所提出的C-CAD系统在实际放射室环境中的效率、准确性和适用性。我们还表明，我们的框架可以推广到更复杂的应用，如前列腺癌多参数磁共振成像(MP-MRI)筛查。
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).
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影响因子：
4.6
作者：
Dou, Qi;Chen, Hao;Heng, Pheng-Ann
通讯作者：
Heng, Pheng-Ann
影响因子：
10.6
作者：
Guo Y;Gao Y;Shen D
通讯作者：
Shen D
影响因子：
5.5
作者：
Drew, Trafton;Evans, Karla;Wolfe, Jeremy M.
通讯作者：
Wolfe, Jeremy M.
影响因子：
6
作者：
Kok, Ellen M.;Jarodzka, Halszka
通讯作者：
Jarodzka, Halszka
DOI：
10.1109/tpami.2006.233
发表时间：
2006-11-01
影响因子：
23.6
作者：
Grady, Leo
通讯作者：
Grady, Leo