The DOI matches its Crossref record; 29 of the 30 references it cites were found in a registry or an index.
Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box
Open accessAbstract
In this paper, we tackle the problem of automatic classification of pulmonary peri-fissural nodules (PFNs). The classification problem is formulated as a machine learning approach, where detected nodule candidates are classified as PFNs or non-PFNs. Supervised learning is used, where a classifier is trained to label the detected nodule. The classification of the nodule in 3D is formulated as an ensemble of classifiers trained to recognize PFNs based on 2D views of the nodule. In order to describe nodule morphology in 2D views, we use the output of a pre-trained convolutional neural network known as OverFeat. We compare our approach with a recently presented descriptor of pulmonary nodule morphology, namely Bag of Frequencies, and illustrate the advantages offered by the two strategies, achieving performance of AUC = 0.868, which is close to the one of human experts.
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“To complement “off-the-shelf” CNN features, [10], [9], [12] all add and integrate various other hand-crafted image features as hybrid inputs for the final CADe classification.”
From Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning (2016) · cites this paper as [12]
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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
Self-citationShin, 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
“There are currently three major techniques that successfully employ CNNs to medical image classification: 1) training the “CNN from scratch” [13], [14], [15], [16], [17]; 2) using “off-the-shelf CNN” features (without retraining the CNN) as complementary information channels to existing hand-crafted image features, fo…”
BackgroundMethodInfluentialDeep 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
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., & Liang, J. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2959609 · Cited by 4,631
Deep Learning Applications in Medical Image Analysis
Ker, J., Wang, L., Rao, J. P., & Lim, C. C. T. (2017) · IEEE Access · doi:10.1109/access.2017.2788044 · Cited by 1,574
Metadata
- Authors
- Ciompi, F., de Hoop, B., van Riel, S. J., Chung, K., Scholten, E. T., Oudkerk, M., de Jong, P. A., Prokop, M., & Ginneken, B. V.
- Affiliations
- Radboud University Medical Center (NL)
- University Medical Center Utrecht (NL)
- University Medical Center Groningen (NL)
- Corresponding author
- Ciompi, Francesco
- ORCID
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2015-12
- Volume
- 26
- Issue
- 1
- Pages
- 195-202
- ISSN
- 1361-8415 (ISSN-L)
- OpenAlex
- W2112467442
- Cited by
- 289
- References
- 30
- Citations by year
- 2026: 10 · 2025: 14 · 2024: 9 · 2023: 18 · 2022: 27 · 2021: 37 · 2020: 36 · 2019: 49 · 2018: 29 · 2017: 40 · 2016: 17 · 2015: 1
- FWCI
- 28.42
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Medical Image Analysis (publishedVersion)
- PubMed (publishedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion)
- Funders
- Netherlands Organization for Scientific Research (639.023.207)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
- Keywords
- nodule classification, computed tomography, convolutional neural networks, lung nodule detection
- MeSH terms
- Algorithms, Solitary Pulmonary Nodule, Humans, Lung Neoplasms, Pattern Recognition, Automated, Radiographic Image Enhancement, Radiographic Image Interpretation, Computer-Assisted, Sensitivity and Specificity, Software, Subtraction Technique, Tomography, X-Ray Computed, Reproducibility of Results, Neural Networks, Computer, Imaging, Three-Dimensional
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