Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
| العنوان: | Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning |
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| المؤلفون: | Daniel J. Mollura, Hoo-Chang Shin, Ronald M. Summers, Isabella Nogues, Jianhua Yao, Ziyue Xu, Mingchen Gao, Le Lu, Holger R. Roth |
| المصدر: | IEEE Transactions on Medical Imaging. 35:1285-1298 |
| بيانات النشر: | Institute of Electrical and Electronics Engineers (IEEE), 2016. |
| سنة النشر: | 2016 |
| مصطلحات موضوعية: | FOS: Computer and information sciences, Databases, Factual, Computer science, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Context (language use), 02 engineering and technology, Machine learning, computer.software_genre, Convolutional neural network, Article, 030218 nuclear medicine & medical imaging, Image (mathematics), 03 medical and health sciences, 0302 clinical medicine, Image Interpretation, Computer-Assisted, 0202 electrical engineering, electronic engineering, information engineering, Humans, Diagnosis, Computer-Assisted, Electrical and Electronic Engineering, Training set, Radiological and Ultrasound Technology, Contextual image classification, Artificial neural network, business.industry, Reproducibility of Results, Computer Science Applications, Feature (computer vision), 020201 artificial intelligence & image processing, Lymph Nodes, Neural Networks, Computer, Artificial intelligence, Lung Diseases, Interstitial, Transfer of learning, business, computer, Software |
| الوصف: | Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and the revival of deep CNN. CNNs enable learning data-driven, highly representative, layered hierarchical image features from sufficient training data. However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off-the-shelf pre-trained CNN features, and conducting unsupervised CNN pre-training with supervised fine-tuning. Another effective method is transfer learning, i.e., fine-tuning CNN models pre-trained from natural image dataset to medical image tasks. In this paper, we exploit three important, but previously understudied factors of employing deep convolutional neural networks to computer-aided detection problems. We first explore and evaluate different CNN architectures. The studied models contain 5 thousand to 160 million parameters, and vary in numbers of layers. We then evaluate the influence of dataset scale and spatial image context on performance. Finally, we examine when and why transfer learning from pre-trained ImageNet (via fine-tuning) can be useful. We study two specific computer-aided detection (CADe) problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification. We achieve the state-of-the-art performance on the mediastinal LN detection, with 85% sensitivity at 3 false positive per patient, and report the first five-fold cross-validation classification results on predicting axial CT slices with ILD categories. Our extensive empirical evaluation, CNN model analysis and valuable insights can be extended to the design of high performance CAD systems for other medical imaging tasks. |
| تدمد: | 1558-254X 0278-0062 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9969ed07d70e717fa84f60ddcced8444 https://doi.org/10.1109/tmi.2016.2528162 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....9969ed07d70e717fa84f60ddcced8444 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 1558254X 02780062 |
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