A collaborative computer aided diagnosis (C-CAD) system with eye-tracking, sparse attentional model, and deep learning

Khosravan, Naji; Çelik, Haydar; Türkbey, Barış; Jones, Elizabeth; Wood, Bradford J.; Bağcı, Ulaş

doi:10.1016/j.media.2018.10.010

Cited by 79 publications

(56 citation statements)

References 49 publications

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“…Hence, developing a Computer-Aided Cardiac Diagnosis (CACD) system helps clinicians as a second opinion in diagnosis [9]. CACD systems are appropriate tools for the decline the diagnostic errors in the clinical domain [10].…”

Section: Introductionmentioning

confidence: 99%

A Multitier Deep Learning Model for Arrhythmia Detection

Hammad

Iliyasu

Subaşı

et al. 2021

IEEE Trans. Instrum. Meas.

172

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Electrocardiograph (ECG) is employed as a primary tool for diagnosing cardiovascular diseases (CVD) in the hospital, which often helps in the early detection of such ailments. ECG signals provide a framework to probe the underlying properties and enhance the initial diagnosis obtained via traditional tools and patient-doctor dialogues. It provides cardiologists with inferences regarding more serious cases. Notwithstanding its proven utility, deciphering large datasets to determine appropriate information remains a challenge in ECGbased CVD diagnosis and treatment. Our study presents a deep neural network (DNN) strategy to ameliorate the aforementioned difficulties. Our strategy consists of a learning stage where classification accuracy is improved via a robust feature extraction. This is followed using a genetic algorithm (GA) process to aggregate the best combination of feature extraction and classification. The MIT-BIH Arrhythmia was employed in the validation to identify five arrhythmia categories based on the association for the advancement of medical instrumentation (AAMI) standard. The performance of the proposed technique alongside state-of-the-art in the area shows an increase of 0.94 and 0.953 in terms of average accuracy and F1 score, respectively. The proposed model could serve as an analytic module to alert users and/or medical experts when anomalies are detected in the acquired ECG data in a smart healthcare framework.

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Section: Introductionmentioning

confidence: 99%

A Multitier Deep Learning Model for Arrhythmia Detection

Hammad

Iliyasu

Subaşı

et al. 2021

IEEE Trans. Instrum. Meas.

172

View full text Add to dashboard Cite

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“…Moreover, [198] developed an eye-tracking interface that is out of scope of this survey. The eye-tracking data and a CAD system are unified using an algorithm that involves graph-based clustering and sparsification, in order to interpret gaze patterns both quantitatively and qualitatively.…”

Section: Lung Cancermentioning

confidence: 99%

Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

Farhat

Sakr

Kilany

2020

Machine Vision and Applications

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Shortly after deep learning algorithms were applied to Image Analysis, and more importantly to medical imaging, their applications increased significantly to become a trend. Likewise, deep learning applications (DL) on pulmonary medical images emerged to achieve remarkable advances leading to promising clinical trials. Yet, coronavirus can be the real trigger to open the route for fast integration of DL in hospitals and medical centers. This paper reviews the development of deep learning applications in medical image analysis targeting pulmonary imaging and giving insights of contributions to COVID-19. It covers more than 160 contributions and surveys in this field, all issued between February 2017 and May 2020 inclusively, highlighting various deep learning tasks such as classification, segmentation, and detection, as well as different pulmonary pathologies like airway diseases, lung cancer, COVID-19 and other infections. It summarizes and discusses the current state-of-the-art approaches in this research domain, highlighting the challenges, especially with COVID-19 pandemic current situation.

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“…Shen et al designed multicrop and multiscale CNNs for lung nodule malignancy suspiciousness classification. Recently, Khosravan et alcombined eye tracking, sparse attention, and deep learning to build a CAD system for both detection and diagnosis.…”

Section: Related Workmentioning

confidence: 99%

Feature‐shared adaptive‐boost deep learning for invasiveness classification of pulmonary subsolid nodules in CT images

Wang

Chen

et al. 2020

Medical Physics

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Purpose: In clinical practice, invasiveness is an important reference indicator for differentiating the malignant degree of subsolid pulmonary nodules. These nodules can be classified as atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IAC). The automatic determination of a nodule's invasiveness based on chest CT scans can guide treatment planning. However, it is challenging, owing to the insufficiency of training data and their interclass similarity and intraclass variation. To address these challenges, we propose a two-stage deep learning strategy for this task: prior-feature learning followed by adaptive-boost deep learning. Methods: The adaptive-boost deep learning is proposed to train a strong classifier for invasiveness classification of subsolid nodules in chest CT images, using multiple 3D convolutional neural network (CNN)-based weak classifiers. Because ensembles of multiple deep 3D CNN models have a huge number of parameters and require large computing resources along with more training and testing time, the prior-feature learning is proposed to reduce the computations by sharing the CNN layers between all weak classifiers. Using this strategy, all weak classifiers can be integrated into a single network. Results: Tenfold cross validation of binary classification was conducted on a total of 1357 nodules, including 765 noninvasive (AAH and AIS) and 592 invasive nodules (MIA and IAC). Ablation experimental results indicated that the proposed binary classifier achieved an accuracy of 73:4% AE 1:4 with an AUC of 81.3% AE 2:2. These results are superior compared to those achieved by three experienced chest imaging specialists who achieved an accuracy of 69:1% , 69:3% , and 67:9% , respectively. About 200 additional nodules were also collected. These nodules covered 50 cases for each category (AAH, AIS, MIA, and IAC, respectively). Both binary and multiple classifications were performed on these data and the results demonstrated that the proposed method definitely achieves better performance than the performance achieved by nonensemble deep learning methods. Conclusions: It can be concluded that the proposed adaptive-boost deep learning can significantly improve the performance of invasiveness classification of pulmonary subsolid nodules in CT images, while the prior-feature learning can significantly reduce the total size of deep models. The promising results on clinical data show that the trained models can be used as an effective lung cancer screening tool in hospitals. Moreover, the proposed strategy can be easily extended to other similar classification tasks in 3D medical images.

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A collaborative computer aided diagnosis (C-CAD) system with eye-tracking, sparse attentional model, and deep learning

Cited by 79 publications

References 49 publications

A Multitier Deep Learning Model for Arrhythmia Detection

A Multitier Deep Learning Model for Arrhythmia Detection

Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

Feature‐shared adaptive‐boost deep learning for invasiveness classification of pulmonary subsolid nodules in CT images

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