The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning
Open accessAbstract
Extracting, harvesting, and building large-scale annotated radiological image datasets is a greatly important yet challenging problem. Meanwhile, vast amounts of clinical annotations have been collected and stored in hospitals' picture archiving and communication systems (PACS). These types of annotations, also known as bookmarks in PACS, are usually marked by radiologists during their daily workflow to highlight significant image findings that may serve as reference for later studies. We propose to mine and harvest these abundant retrospective medical data to build a large-scale lesion image dataset. Our process is scalable and requires minimum manual annotation effort. We mine bookmarks in our institute to develop DeepLesion, a dataset with 32,735 lesions in 32,120 CT slices from 10,594 studies of 4,427 unique patients. There are a variety of lesion types in this dataset, such as lung nodules, liver tumors, enlarged lymph nodes, and so on. It has the potential to be used in various medical image applications. Using DeepLesion, we train a universal lesion detector that can find all types of lesions with one unified framework. In this challenging task, the proposed lesion detector achieves a sensitivity of 81.1% with five false positives per image.
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“Pre-processing using this method resulted in significantly improved segmentation performance on both deep learning (HNN) and non deep learning (GrabCut) approaches on the DeepLesion dataset Yan et al. (2018).”
From Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation (2020) · cites this paper as Yan et al. (2018)
In the citing paper
“Transfer learning from the public large-scale DeepLesion19 CT lesion dataset was effective to handle the data insufficiency issue for developing the deep learning detector with COVID-19 data in this study.”
From Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study (2021) · cites this paper as [19]
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“The dataset is currently the most comprehensive open-source data for CT lesions, that includes 32,120 axial CT slices (from body parts of liver, lung, mediastinum, kidney, pelvis, bone, abdomen, and soft tissue) from 10,594 studies of 4427 unique patients.”
From Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study (2021) · cites this paper as [19]
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The future of digital health with federated learning
Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00323-1 · Cited by 4,015
Self-citationZhou, S. K., Greenspan, H. K., Davatzikos, C. A., Duncan, J. S., van Ginneken, B., Madabhushi, A., Prince, J. L., Rueckert, D., & Summers, R. M. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3054390 · Cited by 1,373
Self-citationRecent advances and clinical applications of deep learning in medical image analysis
Chen, X., Wang, X., Zhang, K., Fung, K.-M. A., Thai, T., Moore, K. N., Mannel, R. S., Liu, H., Zheng, B., & Qiu, Y. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102444 · Cited by 1,180
Self-citationEmbracing imperfect datasets: A review of deep learning solutions for medical image segmentation
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“comparable to Yang et al. (2017), but are obtained using datasets that are two orders of magnitude larger in size.”
Preparing Medical Imaging Data for Machine Learning
Willemink, M. J., Koszek, W. A., Hardell, C., Wu, J., Fleischmann, D., Harvey, H., Folio, L. R., Summers, R. M., Rubin, D. L., & Lungren, M. P. (2020) · Radiology · doi:10.1148/radiol.2020192224 · Cited by 1,020
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Metadata
- Authors
- Yan, K., Wang, X., Lu, L., & Summers, R. M.
- Affiliations
- National Institutes of Health Clinical Center (US)
- National Institutes of Health Clinical Ctr.
- Venue
- Journal of medical imaging
- Publisher
- SPIE-Intl Soc Optical Eng
- Published
- 2018-07-20
- Volume
- 5
- Issue
- 03
- Pages
- 1-11
- ISSN
- 2329-4302 (ISSN-L)
- 2329-4310
- PMC
- PMC6052252
- OpenAlex
- W2883683269
- Cited by
- 573
- References
- 30
- Citations by year
- 2026: 65 · 2025: 81 · 2024: 75 · 2023: 81 · 2022: 78 · 2021: 88 · 2020: 66 · 2019: 36 · 2018: 3
- FWCI
- 27.16
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- other-oa
- Locations
- Journal of medical imaging (publishedVersion, other-oa)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Intramural Research Program of NIH, Clinical Center
- 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
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- lesion detection, CT images, pulmonary nodules, liver tumors, lymphadenopathy, DeepLesion
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