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Gastric Pathology Image Classification Using Stepwise Fine-Tuning for Deep Neural Networks
المؤلفون المشاركون
Nosato, Hirokazu
Sakanashi, Hidenori
Qu, Jia
Hiruta, Nobuyuki
Terai, Kensuke
Murakawa, Masahiro
المصدر
Journal of Healthcare Engineering
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-06-21
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
Deep learning using convolutional neural networks (CNNs) is a distinguished tool for many image classification tasks.
Due to its outstanding robustness and generalization, it is also expected to play a key role to facilitate advanced computer-aided diagnosis (CAD) for pathology images.
However, the shortage of well-annotated pathology image data for training deep neural networks has become a major issue at present because of the high-cost annotation upon pathologist’s professional observation.
Faced with this problem, transfer learning techniques are generally used to reinforcing the capacity of deep neural networks.
In order to further boost the performance of the state-of-the-art deep neural networks and alleviate insufficiency of well-annotated data, this paper presents a novel stepwise fine-tuning-based deep learning scheme for gastric pathology image classification and establishes a new type of target-correlative intermediate datasets.
Our proposed scheme is deemed capable of making the deep neural network imitating the pathologist’s perception manner and of acquiring pathology-related knowledge in advance, but with very limited extra cost in data annotation.
The experiments are conducted with both well-annotated gastric pathology data and the proposed target-correlative intermediate data on several state-of-the-art deep neural networks.
The results congruously demonstrate the feasibility and superiority of our proposed scheme for boosting the classification performance.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Qu, Jia& Hiruta, Nobuyuki& Terai, Kensuke& Nosato, Hirokazu& Murakawa, Masahiro& Sakanashi, Hidenori. 2018. Gastric Pathology Image Classification Using Stepwise Fine-Tuning for Deep Neural Networks. Journal of Healthcare Engineering،Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1191322
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Qu, Jia…[et al.]. Gastric Pathology Image Classification Using Stepwise Fine-Tuning for Deep Neural Networks. Journal of Healthcare Engineering No. 2018 (2018), pp.1-13.
https://search.emarefa.net/detail/BIM-1191322
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Qu, Jia& Hiruta, Nobuyuki& Terai, Kensuke& Nosato, Hirokazu& Murakawa, Masahiro& Sakanashi, Hidenori. Gastric Pathology Image Classification Using Stepwise Fine-Tuning for Deep Neural Networks. Journal of Healthcare Engineering. 2018. Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1191322
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1191322
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
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