Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

By Hoo-Chang Shin, Holger R. Roth, Mingchen Gao, Le Lu, Ziyue Xu, Isabella Nogues, Jianhua Yao, Daniel J. Mollura, Ronald M. Summers. 2016

5,088 citations recorded by the source corpus.

Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly representative, 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, 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.

Subfields: Computer Vision and Pattern Recognition, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

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ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenes (2021, methodology): “Experiments It has been shown that pretraining the model on a large-scale dataset, such as MS COCO [35], improves generalization and helps to prevent overﬁtting [23, 26, 52].”
A survey of data fusion in smart city applications (2019, background): “Alternative solution to the increased deployment cost is to use transfer learning [263], where the knowledge from existing domain can be transferred to other domain to learn from it.”
An overview of deep learning in medical imaging focusing on MRI (2018, background): “seizures [17] and cardiac arrests [18], to computer-aided detection [19] and diagnosis [20] supporting clinical decision making and survival analysis [21], to drug discovery [22] and as an aid in therapy selection and pharmacogenomics [23], to increased operational efficiency [24], stratified care d”
Opportunities and obstacles for deep learning in biology and medicine (2017, background): “[53] compared various deep network architectures, dataset characteristics, and training procedures for computer tomography-based (CT) abnormality detection.”
Measuring the tendency of CNNs to Learn Surface Statistical Regularities (2017, background): “The problems of transfer learning [44, 37, 38] and domain adaptation [35, 10] both investigate the generalization ability of deep neural networks.”
A Comprehensive Survey on Transfer Learning (2019, methodology): “[169] finetuned the pretrained deep neural network for solving the computer-aided detection problems.”
Communication-Efficient Federated Deep Learning With Layerwise Asynchronous Model Update and Temporally Weighted Aggregation (2019, methodology): “In recent years, DNNs have been successfully applied to many complex problem-solvings, including text classiﬁcation, image classiﬁcation, and speech recognition [4], [5], [6].”
Detection and Labeling of Vertebrae in MR Images Using Deep Learning with Clinical Annotations as Training Data (2017, background): “Thus far, transfer learning has been shown to provide promising results [26, 27].”
Accurate brain tumor detection using deep convolutional neural network (2022, background): “Such knowledge helps the model to achieve high accuracy on a small dataset [23].”