The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks
Open accessAbstract
Interstitial lung diseases (ILD) involve several abnormal imaging patterns observed in computed tomography (CT) images. Accurate classification of these patterns plays a significant role in precise clinical decision making of the extent and nature of the diseases. Therefore, it is important for developing automated pulmonary computer-aided detection systems. Conventionally, this task relies on experts' manual identification of regions of interest (ROIs) as a prerequisite to diagnose potential diseases. This protocol is time consuming and inhibits fully automatic assessment. In this paper, we present a new method to classify ILD imaging patterns on CT images. The main difference is that the proposed algorithm uses the entire image as a holistic input. By circumventing the prerequisite of manual input ROIs, our problem set-up is significantly more difficult than previous work but can better address the clinical workflow. Qualitative and quantitative results using a publicly available ILD database demonstrate state-of-the-art classification accuracy under the patch-based classification and shows the potential of predicting the ILD type using holistic image.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network
Prasoon, A., Petersen, K., Igel, C., Lauze, F., Dam, E., & Nielsen, M. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40763-5_31 · Cited by 582
Building a reference multimedia database for interstitial lung diseases
Depeursinge, A., Vargas, A., Platon, A., Geissbuhler, A., Poletti, P.-A., & Müller, H. (2012) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2011.07.003 · Cited by 248 · cited 1×
“A publicly available ILD database has been released recently [5] to improve the detection and classification of a wide range of lung abnormal imaging patterns.”
MethodSelf-citationLarge Margin Local Estimate With Applications to Medical Image Classification
Song, Y., Cai, W., Huang, H., Zhou, Y., Feng, D. D., Wang, Y., Fulham, M. J., & Chen, M. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2393954 · Cited by 87
Cited by
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528162 · Cited by 5,759
Self-citationDeep learning in medical image analysis
Shen, D., Wu, G., & Suk, H.-I. (2017) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071516-044442 · Cited by 4,968
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., & Liang, J. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2535302 · Cited by 3,277
Raghu, G., Remy-Jardin, M., Richeldi, L., Thomson, C. C., Inoue, Y., Johkoh, T., Kreuter, M., Lynch, D. A., Maher, T. M., Martinez, F. J., et al. (2022) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.202202-0399st · Cited by 2,957
Metadata
- Authors
- Gao, M., Bagci, U., Lu, L., Wu, A., Buty, M., Shin, H.-C., Roth, H., Papadakis, G. Z., Depeursinge, A., Summers, R. M., Xu, Z., & Mollura, D. J.
- Venue
- Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization
- Publisher
- Informa UK Limited
- Published
- 2016-06-06
- Volume
- 6
- Issue
- 1
- Pages
- 1-6
- ISSN
- 2168-1163 (ISSN-L)
- 2168-1171
- OpenAlex
- W2467126091
- Cited by
- 259
- References
- 4
- FWCI
- 26.02
- Type
- journal article
- Language
- en
- Open access
- green
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- Gao, M., Bagci, U., Lu, L., Wu, A., Buty, M., Shin, H.-C., Roth, H., Papadakis, G. Z., Depeursinge, A., Summers, R. M., Xu, Z., & Mollura, D. J. (2018). Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization. https://doi.org/10.1080/21681163.2015.1124249
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