The DOI matches its Crossref record; 11 of the 13 references it cites were found in a registry or an index.
Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans
Abstract
Convolutional neural networks (CNNs) have emerged as the most powerful technique for a range of different tasks in computer vision. Recent work suggested that CNN features are generic and can be used for classification tasks outside the exact domain for which the networks were trained. In this work we use the features from one such network, OverFeat, trained for object detection in natural images, for nodule detection in computed tomography scans. We use 865 scans from the publicly available LIDC data set, read by four thoracic radiologists. Nodule candidates are generated by a state-of-the-art nodule detection system. We extract 2D sagittal, coronal and axial patches for each nodule candidate and extract 4096 features from the penultimate layer of OverFeat and classify these with linear support vector machines. We show for various configurations that the off-the-shelf CNN features perform surprisingly well, but not as good as the dedicated detection system. When both approaches are combined, significantly better results are obtained than either approach alone. We conclude that CNN features have great potential to be used for detection tasks in volumetric medical data.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“It is likely that artificial intelligence (AI) technologies will be incorporated into clinical practise, with applications such as image-based diagnostics in radiology demonstrating accuracy and efficacy rivalling speciality trained physicians [1–2].”
From Exploration of exposure to artificial intelligence in undergraduate medical education: a Canadian cross-sectional mixed-methods study (2022) · cites this paper as [2]
In the citing paper
“It is likely that artificial intelligence (AI) technologies will be incorporated into clinical practise, with applications such as image-based diagnostics in radiology demonstrating accuracy and efficacy rivalling speciality trained physicians [1–2].”
From Exploration of exposure to artificial intelligence in undergraduate medical education: a Canadian cross-sectional mixed-methods study (2022) · cites this paper as [2]
In the citing paper
“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 [9]
Citations
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
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening
The National Lung Screening Trial Research Team (2011) · New England Journal of Medicine · doi:10.1056/nejmoa1102873 · Cited by 11,064
CNN Features Off-the-Shelf: An Astounding Baseline for Recognition
Razavian, A. S., Azizpour, H., Sullivan, J., & Carlsson, S. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2014.131 · Cited by 4,451
An analysis of single-layer networks in unsupervised feature learning
Coates, A., Ng, A. Y., & Lee, H. (2011) · Cited by 2,760
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., & LeCun, Y. (2013) · arXiv · doi:10.48550/arxiv.1312.6229 · Cited by 2,680
CNN Features off-the-shelf: an Astounding Baseline for Recognition
Razavian, A. S., Azizpour, H., Sullivan, J., & Carlsson, S. (2014) · arXiv · doi:10.48550/arxiv.1403.6382 · Cited by 607
Roth, H. R., Lu, L., Seff, A., Cherry, K. M., Hoffman, J., Wang, S., Liu, J., Turkbey, E., & Summers, R. M. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10404-1_65 · Cited by 355
Computer-aided Diagnosis: How to Move from the Laboratory to the Clinic
van Ginneken, B., Schaefer-Prokop, C. M., & Prokop, M. (2011) · Radiology · doi:10.1148/radiol.11091710 · Cited by 306
van Ginneken, B., Armato, S. G., de Hoop, B., de Vorst, S. V. A.-V., Duindam, T., Niemeijer, M., Murphy, K., Schilham, A. M. R., Retico, A., Fantacci, M. E., et al. (2010) · Medical Image Analysis · doi:10.1016/j.media.2010.05.005 · Cited by 298
Research works printed but not linked: 2
- jacobs, 2013, Benchmarking computer-aided detection of pulmonary nodules on the recently completed publicly available LIDC/IDRI database, Radiological Society of North America Annual Meeting
- krizhevsky, 2012, Ima-genet classification with deep convolutional neural net-works, NIPS, 1097
Cited by
Sorted by citations
Items 201–278 of 278
Ensemble of Handcrafted and Deep Learned Features for Cervical Cell Classification
Nanni, L., Ghidoni, S., Brahnam, S., Liu, S., & Zhang, L. (2020) · Intelligent systems reference library · doi:10.1007/978-3-030-42750-4_4 · Cited by 5
An Improved YOLO V5 Model for Pulmonary Nodule Detection with Synthetic Data Generated by GAN
Zhang, J., & Chung, T. (2022) · IEEE Smart World Congress (SWC) · doi:10.1109/smartworld-uic-atc-scalcom-digitaltwin-pricomp-metaverse56740.2022.00181 · Cited by 4
Chowdhury, A., Sevinsky, C. J., Santamaría-Pang, A., & Yener, B. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2255687 · Cited by 4
“Secondly, transfer learning using CNNs have proved to be an effecting strategy [5-7].”
BackgroundAutomatic Lung Nodule Detection in CT Images Using Convolutional Neural Networks
Shaukat, F., Javed, K., Raja, G., Mir, J., & Shahid, M. L. U. R. (2019) · IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences · doi:10.1587/transfun.e102.a.1364 · Cited by 4
Gram staining of intestinal flora classification based on convolutional neural network
Xu, X., Qin, J., & Guo, J. (2017) · IEEE International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/healthcom.2017.8210819 · Cited by 3
“Many Computer-aided detection(CAD) [1] systems are well designed to help radiologists for imaging reading [2], drawing the gross tumor volume (GTV)[3][4] and reducing the misdiagnosis.”
BackgroundOrientation regression in hand radiographs: a transfer learning approach
Baltruschat, I. M., Saalbach, A., Heinrich, M. P., Nickisch, H., & Jockel, S. (2018) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.2291620 · Cited by 4
Edwards, B. I., Khougali, N., & Cheok, A. D. (2017) · Preprints.org · doi:10.20944/preprints201710.0117.v1 · Cited by 4
Artificial Neural Networks in Medicine
Kocsis, Z. T. (2019) · Acta Technica Jaurinensis · doi:10.14513/actatechjaur.v12.n2.497 · Cited by 3
“[5] [13] [14] In all cases, CT images are made in 2 dimensions, which represent the patient in horizontal or vertical sections, slices.”
BackgroundCOVID-19 Detection from X-ray Images Using a New CNN Approach
Saglam, S., Kumrular, R. K., & Aysel, H. I. (2022) · IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) · doi:10.1109/sceecs54111.2022.9741052 · Cited by 3
Hazra, A. (2021) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-021-00205-6 · Cited by 3
Paediatric Frontal Chest Radiograph Screening with Fine-Tuned Convolutional Neural Networks
Gerrand, J., Williams, Q., Lunga, D., Pantanowitz, A., Madhi, S. A., & Mahomed, N. (2017) · Communications in computer and information science · doi:10.1007/978-3-319-60964-5_74 · Cited by 3
Lin, Y., Song, H., Zheng, R., Cai, J., Li, Z., & Li, H. (2019) · doi:10.1117/12.2536709 · Cited by 3
Ghosal, S., & Shah, P. P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.05791 · Cited by 3
“[8, 31, 32, 33, 23] For example, one study [34] claimed that use of pretrained weights for the segmentation of MRI images by fine-tuning the pretrained AlexNet CNN model to classify RGB fused images achieved better performance.”
BackgroundPucchio, A., BHSc, R. R., Caton, N., Gariscsak, P., Papa, J. D., Nabhen, J. J., Vo, V., Lee, W., & de Moraes, F. Y. (2022) · doi:10.21203/rs.3.rs-1759145/v1 · Cited by 2
Manokaran, J., Mittal, R., & Ukwatta, E. (2024) · Journal of medical imaging · doi:10.1117/1.jmi.11.4.044502 · Cited by 2
Hybrid Deep Learning and Machine Learning Approach for Lung Cancer Detection
Kumari, A. R., Kumar, A. S., Reddy, A. S., Prashanthi, G. E., Murali, G., & Rela, M. (2025) · IEEE International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET) · doi:10.1109/acroset66531.2025.11280875 · Cited by 2
“Conversely, ML techniques provide increased interpretability and efficiency but are dependent on manual feature extraction with reduced adaptability in advanced medical imaging tasks [9], [10].”
An adaptive pulmonary nodule detection algorithm
Cao, K., Meng, G., Wang, Z., Liu, Y., Liu, H., & Sun, L. (2020) · Journal of X-Ray Science and Technology · doi:10.3233/xst-200656 · Cited by 2
“Ginneken et al. [14] extracted the coronal, sagittal, and axial faces for each candidate nodule detected by the candidate nodule detection module of the OverFeat model, then used a convolutional neural network to extract features and extract the resulting.”
Cardiothoracic Ratio Calculation and Cardiomegaly Detection Based on Object Detection
Tacyildiz, Z. C., Kılıç, E., BUDAK, A., & Karatas, H. (2021) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu53274.2021.9477946 · Cited by 2
Lung Nodule Segmentation using Deep Learning and Advanced UNet Model
Pawar, P. D., Badjate, S. L., & Gulhane, S. M. (2022) · SAMRIDDHI A Journal of Physical Sciences Engineering and Technology · doi:10.18090/samriddhi.v14spli01.19 · Cited by 2
URDNet: A Unified Regression Network for GGO Detection in Lung CT Images
Liu, W., Ren, Y., & Li, H. (2020) · Wireless Communications and Mobile Computing · doi:10.1155/2020/8862353 · Cited by 2
Intra-Slice Motion Correction of Intravascular OCT Images Using Deep Features
Abdolmanafi, A., Duong, L., Dahdah, N., & Farida Cheriet, A. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2878914 · Cited by 2
“Pre-trained networks have been used as feature generators [31]–[34].”
MethodTransfer and Association: A Novel Detection Method for Targets without Prior Homogeneous Samples
Zhou, G., & Zhang, Y. (2019) · Remote Sensing · doi:10.3390/rs11121492 · Cited by 2
Novel Techniques for Classification of Lung Nodules using Deep Learning Approach
Bhavanishankar, K., & Sudhamani, M. V. (2019) · The Open Biomedical Engineering Journal · doi:10.2174/1874120701913010120 · Cited by 2
“[12] proposed a Convolutional Neural Network based approach to detect the pulmonary nodules in CT scans which generated the sensitivity of 78%.”
Fusion of Multi-Size Candidate Regions Enhances Two-Stage Hippocampus Segmentation
Cao, P., Sheng, Q., Fang, S., Li, X., Ning, G., & Pan, Q. (2020) · IEEE Access · doi:10.1109/access.2020.2984661 · Cited by 2
“In the processing of tissues with complex morphologies, e.g., pulmonary nodule detection [47], [48], knee cartilage segmentation [49], and pancreas segmentation [8], 2D CNN models may be more generalizable than their 3D counterparts.”
BackgroundToennies, K. D. (2017) · Advances in computer vision and pattern recognition · doi:10.1007/978-1-4471-7320-5_12 · Cited by 2
Tian, J., Dong, D., Liu, Z., & Wei, J. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-818101-0.00004-5 · Cited by 3
A Pulmonary Nodule Spiculation Recognition Algorithm Based on Generative Adversarial Networks
Zhang, J., Qiu, S., Cui, X., & Liang, T. B. (2022) · BioMed Research International · doi:10.1155/2022/3341924 · Cited by 2
Hybrid Optimized Learning for Lung Cancer Classification
Vidhya, R., & Mirnalinee, T. T. (2022) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2022.025060 · Cited by 2
Pulmonary Nodule Detection Based on Multi-branch 3D Squeeze-and-Excitation Network
Zhang, Y., Zhang, J., Che, C., Lin, K. Y., Zhou, D., Zhang, Q., & Wei, X. (2019) · IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · doi:10.1109/dasc/picom/cbdcom/cyberscitech.2019.00101 · Cited by 2
“[23] 865 0.”
ResultVeith, A. P., & Baker, A. B. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.06.028555 · Cited by 2
An Adaptive Enhancement Based Hybrid CNN Model for Digital Dental X-ray Positions ClassificationPreprint
Wang, Y., Sun, L., Zhang, Y., Lv, D., Li, Z., & Qi, W. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.01509 · Cited by 2
“[6] Bram Van Ginneken, Arnaud AA Setio, Colin Jacobs, and Francesco Ciompi.”
BackgroundMethodAzhagarasan, A. K., Bhaskaran, P., Ramachandran, A., & Sivalingam, K. (2025) · Exploration of Medicine · doi:10.37349/emed.2025.1001341 · Cited by 1
Dual-path deep learning framework for accurate and interpretable brain tumor diagnosis
Haroun, R. F., Khater, H. A. A., Mohamed, M. A., & Abdelfattah, M. G. (2026) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-026-03367-7 · Cited by 1
“Modern systems leverage transfer learning [20, 21] and fine-tuning [22] to bridge the semantic gap between low-level features and high-level diagnostic understanding [23].”
Nalepa, J., Czardybon, M., & Walczak, M. (2018) · doi:10.1117/12.2303955 · Cited by 1
“There are three main approaches for employing deep learning in the context of medical image segmentation and classification: unsupervised training with supervised fine tuning,(21) training DNNs from scratch,(22) and using pre-trained features.(23) Since DNNs are data-driven approaches, appropriate data pre-processing…”
MethodMasood, A., Sheng, B., Li, P., Yang, P., & Kim, J. (2019) · IEEE International Conference on Industrial Informatics (INDIN) · doi:10.1109/indin41052.2019.8972133 · Cited by 1
Collaborative Artificial Intelligence Algorithms for Medical Imaging Applications
Khosravan, N. (2019) · Journal of International Crisis and Risk Communication Research · Cited by 1
Hu, Y., Xu, Y. X., Huang, X., Gan, D., Huang, H., Shao, L., Cheng, Q., & Xianbo, D. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-93046-2_12 · Cited by 1
Khadidos, A. O. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-4052-1_32 · Cited by 1
Lung Cancer Diagnosis Using Deep Convolutional Neural Network
Basra, J., Gupta, E., Saini, M., & Chhabra, A. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-9090-8_32 · Cited by 1
Khanom, M., Iqbal, A., Hoque, A., Jeenia, F. T., & Uddin, M. (2023) · Journal of Medicine · doi:10.3329/jom.v24i2.67273 · Cited by 1
Lung Tumor Classification Optimizer with Augment Input Images
Praveena, R., Babu, T. R. G., Mahalakshmi, V., Sangeetha, C., & Shobana, V. (2024) · Journal of Innovative Image Processing · doi:10.36548/jiip.2024.2.010 · Cited by 1
Lung cancer prediction using random forest, image segmentation and CNN
Kaushal, K., & Balasaigayathri, B. (2024) · AIP conference proceedings · doi:10.1063/5.0219173 · Cited by 1
Deng, B., Abidin, A. Z., DSouza, A. M., Nagarajan, M., Coan, P., & Wismüller, A. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2254645 · Cited by 1
Challenges and Technological Trends Toward Improved Medical Imaging-based Predictive Data-mining
Zaritsky, A. (2018) · EBioMedicine · doi:10.1016/j.ebiom.2018.08.017 · Cited by 1
Idiopathic Interstitial Pneumonias Medical Image Detection Using Deep Learning Techniques
Sun, Z., & He, S. (2019) · ACM Southeast Regional Conference (ACMSE) · doi:10.1145/3299815.3314425 · Cited by 1
“[9, 28] by cutting out multiple faces of the nodule, such as extracting the orthogonal isosceles of the nodule to preserve the three-dimensional characteristics of the nodule, the method will lose most of the solid of the nodule Information.”
MethodAbdelrahman, S. A., Abdelwahab, M. M., & Sayed, M. S. (2019) · IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) · doi:10.1109/mwscas.2019.8885090 · Cited by 1
“In addition, employing the concatenated image excludes the fusion stage [5] which was employed in the existing methods to take the final decision from three CNNs.”
MethodCarvalho, E. D., & Carvalho, E. D. (2020) · Escola Regional de Computação Ceará, Maranhão, Piauí (ERCEMAPI) · doi:10.5753/ercemapi.2020.11493 · Cited by 1
Deep Domain Adaptation Learning Framework for Associating Image Features to Tumour Gene Profile
Xia, T. (2018) · The Sydney eScholarship Repository (The University of Sydney) · Cited by 1
Towards an affordable deep learning system: automated intervertebral disc detection in x-ray images
Sa, R., Owens, W., Wiegand, R., & Chaudhary, V. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2254692
“Using pretrained model as feature extractor has shown promising result for moderate sized medical data.(2) However, most work have extracted features from the last layer and little has been explored in terms of the number of convolutional layers needed for best performance.”
MethodLearning to Generate High Resolution Images with Bilateral Adversarial Networks
Zhou, Y., & Nwogu, I. (2017) · International Conference on Advances in Image Processing (ICAIP) · doi:10.1145/3133264.3133289
Hierarchical Fusion Based Deep Learning Framework for Lung Nodule Classification
Şekeroğlu, K. (2017) · doi:10.31390/gradschool_dissertations.4115
“Once the class scores from each CNN is computed, they are fused using a product rule on the output probabilities [26].”
MethodMore or Less? Predict the Social Influence of Malicious URLs on Social MediaPreprint
Lai, C.-M., Wang, X., Chapman, J. W., Lin, Y., Ho, Y.-C., Wu, S. F., McDaniel, P. D., & Çam, H. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.02978
“s that have different ideas [2]. Many voting behaviors are related to this effect, voters may or may not follow their own conscience to make a voting decision but may just follow the majority opinion [26]. From the result obtained by [16], when considering the posts targeted with a comment that includes a malicious UR…”
BackgroundZhang, G., Kong, L., Guo, W., Guo, J. S., Zhu, Z., Kim, Y., & Gong, Z. (2018) · International Symposium on Image Computing and Digital Medicine (ISICDM) · doi:10.1145/3285996.3286010
“In recent years, convolutional neural network (CNN) or ConvNets have also been introduced in the field of medical image analysis[5-10].”
BackgroundFarhangi, M. M. (2019) · ThinkIR: The University of Louisville's Institutional Repository (University of Louisville) · doi:10.18297/etd/3301
“Authors in [93] extracted 2-D mid-axial, mid-coronal, and mid-sagittal planes from 3-D thoracic CT scans and builting an RGB-like image with the planes and feed it to OverFeat network [73] to detect lung nodules from other thoracic lesions.”
Background3D-CNN Based Computer-Aided Diagnosis (CADx) for Lung Nodule Diagnosis
Tai, T.-C., Tian, M., Cho, W.-T., & Lai, C. (2020) · Communications in computer and information science · doi:10.1007/978-981-15-6113-9_5
Computer vision based smoke and fire detection for outdoor environments
Alamgir, N. (2020) · Queensland University of Technology · doi:10.5204/thesis.eprints.201654
Pulmonary Nodule Detection and False Acceptance Reduction: Review
Pawar, S., & Patil, B. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-5679-1_58
PiaNet: A pyramid input augmented convolutional neural network for GGO detection in 3D lung CT scansPreprint
Liu, W., Liu, X., Luó, X., Wang, M., Han, G., Zhao, X., & Zhu, Z. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.05267
“[13] presented promising results of nodule extraction using an off-the-shelf convolutional neural network (CNN) that was pre-trained on a natural image recognition task.”
MethodLiang, S., Liu, H., Yang, F., Qin, C., & Feng, Y. (2021) · Journal of Medical Imaging and Health Informatics · doi:10.1166/jmihi.2021.3448
Classification of chest diseases from x-ray on chexpert dataset
Saleem, H. N. (2020)
Lien, W., Yeh, C., Chang, C.-Y., Chang, C.-H., Chen, C.-H., & Lin, Y. (2022) · doi:10.2196/preprints.41921
Masood, A., Naseem, U., & Nasim, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-45350-2_3
Deep Learning Approaches for Small Data Sets in Medical Imaging
Shekhawat, S. S., & Kumar, S. (2019) · International Journal of Psychosocial Rehabilitation · doi:10.61841/v23i6/400332
Classification of Biomedical Images with Mined Statistical Features and Dynamic Programming
Man, X., & Song, Y. (2024) · Current Computer Science · doi:10.2174/0129503779291012240424070357
Bianconi, F., Fravolini, M. L., Caltana, E., Khan, M. U., & Palumbo, B. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72845-7_16
Validation and Evaluation Matrix
Malik, I. (2024) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-5226-7.ch011
Harikumar, P., Batra, R., & Makhija, P. (2024) · International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI) · doi:10.1109/icscai61790.2024.10866850
Revolutionizing Healthcare: Integrating AI and ML for Precision Medicine and Operational Efficiency
Nagam, M. R. (2025) · International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA) · doi:10.1109/aimla63829.2025.11041055
Lung cancer detection and classification using alexnet CNN
Thomas, R., Nair, A. J., Jacob, A. M., Mohan, A., & Prakash, A. (2025) · AIP conference proceedings · doi:10.1063/5.0259461
Comparative Machine Learning Models for Sign Language Detection
Dwivedi, A., Nithya, P., & Goenka, A. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-6906-6_14 · Cited by 1
Big Data and Medical Information Processing
Luo, S., & Pan, L. (2026) · Big data management · doi:10.1007/978-981-95-5725-7_6
Resolution-Aware Evidential Fusion for Scale-Invariant Attribution in 3D Lung Nodule Detection
Haddar, B., & Elleuch, M. A. (2026) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-026-02237-y
(2017) · Radboud Repository (Radboud University) · Cited by 284
Self-citation
Metadata
- Authors
- van Ginneken, B., Setio, A. A. A., Jacobs, C., & Ciompi, F.
- Affiliations
- Radboud University Medical Center (NL)
- Venue
- 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)
- Publisher
- IEEE
- Published
- 2015-04
- Pages
- 286-289
- OpenAlex
- W1516595210
- Cited by
- 278
- References
- 13
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 11 · 2023: 9 · 2022: 17 · 2021: 29 · 2020: 48 · 2019: 51 · 2018: 39 · 2017: 43 · 2016: 20 · 2015: 3
- FWCI
- 19.72
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Symposium on Biomedical Imaging (ISBI) (publishedVersion)
- 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
- lung nodule detection, convolutional neural network features, transfer learning, computed tomography, LIDC, volumetric medical data, feature extraction, support vector machine, object detection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
