کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6938105 | 1449922 | 2018 | 35 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Convolutional neural networks: Ensemble modeling, fine-tuning and unsupervised semantic localization for neurosurgical CLE images
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
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![عکس صفحه اول مقاله: Convolutional neural networks: Ensemble modeling, fine-tuning and unsupervised semantic localization for neurosurgical CLE images Convolutional neural networks: Ensemble modeling, fine-tuning and unsupervised semantic localization for neurosurgical CLE images](/preview/png/6938105.png)
چکیده انگلیسی
Confocal laser endomicroscopy (CLE) is an advanced optical fluorescence technology undergoing assessment for applications in brain tumor surgery. Many of the CLE images can be distorted and interpreted as nondiagnostic. However, just one neat CLE image might suffice for intraoperative diagnosis of the tumor. While manual examination of thousands of nondiagnostic images during surgery would be impractical, this creates an opportunity for a model to select diagnostic images for the pathologists or surgeons review. In this study, we sought to develop a deep learning model to automatically detect the diagnostic images. We explored the effect of training regimes and ensemble modeling and localized histological features from diagnostic CLE images. The developed model could achieve promising agreement with the ground truth. With the speed and precision of the proposed method, it has potential to be integrated into the operative workflow in the brain tumor surgery.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Visual Communication and Image Representation - Volume 54, July 2018, Pages 10-20
Journal: Journal of Visual Communication and Image Representation - Volume 54, July 2018, Pages 10-20
نویسندگان
Mohammadhassan Izadyyazdanabadi, Evgenii Belykh, Michael Mooney, Nikolay Martirosyan, Jennifer Eschbacher, Peter Nakaji, Mark C. Preul, Yezhou Yang,