Building a reference multimedia database for interstitial lung diseases
Open accessAbstract
This paper describes the methodology used to create a multimedia collection of cases with interstitial lung diseases (ILDs) at the University Hospitals of Geneva. The dataset contains high-resolution computed tomography (HRCT) image series with three-dimensional annotated regions of pathological lung tissue along with clinical parameters from patients with pathologically proven diagnoses of ILDs. The motivations for this work is to palliate the lack of publicly available collections of ILD cases to serve as a basis for the development and evaluation of image-based computerized diagnostic aid. After 38 months of data collection, the library contains 128 patients affected with one of the 13 histological diagnoses of ILDs, 108 image series with more than 41l of annotated lung tissue patterns as well as a comprehensive set of 99 clinical parameters related to ILDs. The database is available for research on request and after signature of a license agreement.
Integrity signals
Registry
Cites
Statistics
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?
Although the ILD and LTRC datasets were generated and annotated separately, they contain many common disease labels; EM, GG, FB, and MN belong to both datasets and can be jointly trained/tested to form a larger unified dataset.
“Although the ILD [37] and LTRC datasets [64] (used in [19]) were generated and annotated separately, they contain many common disease labels. For instance, the ILD disease classes emphysema (EM), ground glass (GG), fibrosis (FB), and micronodules (MN) belong to both datasets, and thus can be jointly trained/tested to form a larger and unified dataset.”
From Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning (2016) · cites this paper as [37]
Cites
Textures: A Photographic Album for Artists and Designers
Hersey, I., & Brodatz, P. (1968) · Leonardo · doi:10.2307/1571915 · Cited by 2,637 · cited 1× · see citing works
“Populardatasets suchas Lena, Brodatz [8] or Iris1 allow to qualitatively and quantitatively evaluate a large number of basic methods in image processing and machine learning and thereby established a de facto reference dataset.”
BackgroundComputer-aided diagnosis in medical imaging: historical review, current status and future potential
Doi, K. (2007) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2007.02.002 · Cited by 1,855 · cited 1× · see citing works
“On the one hand, the database constitutes a basis for developing omputerized tools [4,5] such as automatic detection of abnormal ulmonary tissue types in high-resolution computed tomography HRCT) images and retrieval of similar cases [6,7].”
MethodMüller, H., Michoux, N., Bandon, D., & Geissbühler, A. (2004) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2003.11.024 · Cited by 1,490 · see citing works
Self-citationIdiopathic Interstitial Pneumonia
Flaherty, K. R., King, T. E., Raghu, G., Lynch, J. P., Colby, T. V., Travis, W. D., Gross, B. H., Kazerooni, E. A., Toews, G. B., Long, Q., et al. (2004) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.200402-147oc · Cited by 672 · see citing works
Evidence for beneficial low level radiation effects and radiation hormesis
Feinendegen, L. E. (2005) · British Journal of Radiology · doi:10.1259/bjr/63353075 · Cited by 469 · see citing works
High-Resolution CT of the Lung
Webb, W. R., Müller, N. L., Naidich, D. P., & Meziane, M. A. (1997) · Journal of Bronchology · doi:10.1097/00128594-199704000-00036 · Cited by 455 · see citing works
Current status and future potential of computer-aided diagnosis in medical imaging
Doi, K. (2005) · British Journal of Radiology · doi:10.1259/bjr/82933343 · Cited by 439 · see citing works
Lung Image Database Consortium: Developing a Resource for the Medical Imaging Research Community
Armato, S. G., McLennan, G., McNitt‐Gray, M. F., Meyer, C. R., Yankelevitz, D. F., Aberle, D. R., Henschke, C. I., Hoffman, E. A., Kazerooni, E. A., MacMahon, H., et al. (2004) · Radiology · doi:10.1148/radiol.2323032035 · Cited by 381 · see citing works
McNitt‐Gray, M. F., Armato, S. G., Meyer, C. R., Reeves, A. P., McLennan, G., Pais, R. C., Freymann, J., Brown, M. S., Engelmann, R. M., Bland, P. H., et al. (2007) · Academic Radiology · doi:10.1016/j.acra.2007.07.021 · Cited by 219 · see citing works
Enhancement of Clinicians' Diagnostic Reasoning by Computer-Based Consultation
Friedman, C. P., Elstein, A. S., Wolf, F. M., Murphy, G. C., Franz, T. M., Heckerling, P. S., Fine, P. L., Miller, T. M., & Abraham, V. (1999) · JAMA · doi:10.1001/jama.282.19.1851 · Cited by 219 · see citing works
Comparison of registries of interstitial lung diseases in three European countries
Thomeer, M., Costabe, U., Rizzato, G., Poletti, V. E., & Demedts, M. (2001) · European Respiratory Journal · doi:10.1183/09031936.01.18s320114 · Cited by 216 · see citing works
HRCT diagnosis of diffuse parenchymal lung disease: inter-observer variation
Aziz, Z. A. (2004) · Thorax · doi:10.1136/thx.2003.020396 · Cited by 190 · see citing works
Aisen, A. M., Broderick, L. S., Winer-Muram, H. T., Brodley, C. E., Kak, A. C., Pavlopoulou, C., Dy, J., Shyu, C., & Marchiori, A. (2003) · Radiology · doi:10.1148/radiol.2281020126 · Cited by 169 · see citing works
Chiyo, M., Sekine, Y., Iwata, T., Tatsumi, K., Yasufuku, K., Iyoda, A., Otsuji, M., Yoshida, S., Shibuya, K., Iizasa, T., et al. (2003) · Journal of Thoracic and Cardiovascular Surgery · doi:10.1016/s0022-5223(03)00791-8 · Cited by 124 · see citing works
Depeursinge, A., Racoceanu, D., Iavindrasana, J., Cohen, G., Platon, A., Poletti, P., & Müller, H. (2010) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2010.04.006 · Cited by 124 · see citing works
Self-citationDetection Failures in Spiral CT Screening for Lung Cancer: Analysis of CT Findings
Kakinuma, R., Ohmatsu, H., Kaneko, M., Eguchi, K., Naruke, T., Nagai, K., Nishiwaki, Y., Suzuki, A., & Moriyama, N. (1999) · Radiology · doi:10.1148/radiology.212.1.r99jn1461 · Cited by 121 · see citing works
Armato, S. G., McNitt‐Gray, M. F., Reeves, A. P., Meyer, C. R., McLennan, G., Aberle, D. R., Kazerooni, E. A., MacMahon, H., van Beek, E. J. R., Yankelevitz, D. F., et al. (2007) · Academic Radiology · doi:10.1016/j.acra.2007.07.008 · Cited by 106 · see citing works
Computer-aided diagnosis in chest radiography
Katsuragawa, S., & Doi, K. (2007) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2007.02.003 · Cited by 104 · cited 1× · see citing works
“On the one hand, the database constitutes a basis for developing omputerized tools [4,5] such as automatic detection of abnormal ulmonary tissue types in high-resolution computed tomography HRCT) images and retrieval of similar cases [6,7].”
MethodStrategies for health data exchange for secondary, cross-institutional clinical research
Elger, B., Iavindrasana, J., Iacono, L., Müller, H., Roduit, N., Summers, P. E., & Wright, J. (2010) · Comput. Methods Programs Biomed. · doi:10.1016/j.cmpb.2009.12.001 · Cited by 101 · see citing works
Self-citationClinical value of high-resolution CT in chronic diffuse lung disease.
Müller, N. L. (1991) · American Journal of Roentgenology · doi:10.2214/ajr.157.6.1950859 · Cited by 93 · see citing works
Idiopathic Pulmonary Fibrosis: Spectrum of High-Resolution CT Findings
Souza, C. A., Müller, N. L., Flint, J., Wright, J. L., & Churg, A. (2005) · American Journal of Roentgenology · doi:10.2214/ajr.04.1599 · Cited by 78 · see citing works
High-resolution CT of the lung
Boender, J. (1992) · European Journal of Radiology · doi:10.1016/0720-048x(92)90150-8 · Cited by 62 · see citing works
Lung Tissue Classification Using Wavelet Frames
Depeursinge, A., Sage, D., Hidki, A., Platon, A., Poletti, P., Unser, M. A., & Müller, H. (2007) · Conference proceedings · doi:10.1109/iembs.2007.4353786 · Cited by 62 · see citing works
Self-citationA reference data set for the evaluation of medical image retrieval systems
Müller, H., Rosset, A., Vallée, J., Terrier, F., & Geissbühler, A. (2004) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2004.04.005 · Cited by 60 · see citing works
Self-citationLutterbey, G. G., Grohé, C., Gieseke, J., Falkenhausen, M. V., Morakkabati, N., Wattjes, M. P., Manka, R., Trog, D., & Schild, H. H. (2006) · European Journal of Radiology · doi:10.1016/j.ejrad.2006.09.005 · Cited by 42 · see citing works
A public image database to support research in computer aided diagnosis
Reeves, A. P., Biancardi, A. M., Yankelevitz, D. F., Fotin, S. V., Keller, B. M., Jirapatnakul, A. C., & Lee, J. (2009) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2009.5334807 · Cited by 40 · see citing works
Depeursinge, A., Vargas, A., Gaillard, F., Platon, A., Geissbühler, A., Poletti, P., & Müller, H. (2011) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-011-0618-9 · Cited by 33 · see citing works
Self-citationApproach to ground-glass opacification of the lung
Nowers, K., Rasband, J. D., Berges, G., & Gosselin, M. V. (2002) · Seminars in Ultrasound CT and MRI · doi:10.1016/s0887-2171(02)90019-3 · Cited by 32 · see citing works
High resolution computed tomography of the lungs
Worthy, S. A. (1995) · BMJ · doi:10.1136/bmj.310.6980.616 · Cited by 27 · see citing works
Bartholmai, B. J., Karwoski, R. A., Zavaletta, V. A., Robb, R. A., & Holmes, D. (2006) · The Insight Journal · doi:10.54294/hzdcno · Cited by 27 · see citing works
3D lung image retrieval using localized features
Depeursinge, A., Zrimec, T., Busayarat, S., & Müller, H. (2011) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.877943 · Cited by 26 · see citing works
Self-citationRajasekaran, H., Iacono, L. L., Hasselmeyer, P., Fingberg, J., Summers, P. E., Siegfried Benkner, A., Engelbrecht, G., Arbona, A., Chiarini, A., Friedrich, C. M., et al. (2008) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2008.42 · Cited by 24 · see citing works
Lowe, H., Buchanan, B. G., Cooper, G. F., & Vries, J. K. (1995) · PubMed Central · Cited by 23 · see citing works
3D Case–Based Retrieval for Interstitial Lung Diseases
Depeursinge, A., Vargas, A., Platon, A., Geissbühler, A., Poletti, P., & Müller, H. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-11769-5_4 · Cited by 20 · see citing works
Self-citationProcessing of CT images for analysis of diffuse lung disease in the lung tissue research consortium
Karwoski, R. A., Bartholmai, B. J., Zavaletta, V. A., Holmes, D., & Robb, R. A. (2008) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.773159 · Cited by 19 · see citing works
INFILTRATIVE AND INTERSTITIAL LUNG DISEASE IN THE ELDERLY PATIENT
Wade, J. F., & King, T. E. (1993) · Clinics in Chest Medicine · doi:10.1016/s0272-5231(21)00912-6 · Cited by 13 · see citing works
High Resolution Computed Tomography of the Lungs
Franquet, T. (2004) · Technical report · doi:10.1016/s1571-5078(04)02002-1 · Cited by 13 · see citing works
Horsch, A., Prinz, M., Schneider, S., Sipilä, O., Spinnler, K. P., Vallée, J., Leeuw, I. M. V., Vogl, R., Wittenberg, T. C., & Zahlmann, G. (2003) · Informatik aktuell · doi:10.1007/978-3-642-18993-7_74 · Cited by 11 · see citing works
Medical Imaging 2008: Physiology, Function, and Structure from Medical Images
Hu, X. P., & Clough, A. V. (2008) · Cited by 11 · see citing works
Lung CT Analysis and Retrieval as a Diagnostic Aid.
Müller, H., Marquis, S., Cohen, G., Lovis, C., & Geissbühler, A. (2005) · PubMed · Cited by 9 · see citing works
Self-citationRudrapatna, M., Sowmya, A., Zrimec, T., Wilson, P., KOSSOFF, G., Lucas, P., Wong, J. S. J., Misra, A., & Busayarat, S. (2003) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.526290 · Cited by 7 · see citing works
Prinz, M., Schneider, S., Sipilä, O., Spinnler, K. P., Vallée, J., Leeuw, I. M. V., Vogl, R., Wittenberg, T. C., Zahlmann, G., & Horsch, A. (2004) · Methods of Information in Medicine · doi:10.1055/s-0038-1633884 · Cited by 7 · cited 1× · see citing works
“These requirements are of high importance when building mage-based computerized diagnostic aid based on medical image rocessing [1,2].”
BackgroundDepeursinge, A. (2010) · Archive ouverte UNIGE (University of Geneva) · doi:10.13097/archive-ouverte/unige:6550 · Cited by 5 · see citing works
Self-citationLung Tissue Analysis Using Isotropic Polyharmonic B-Spline Wavelets
Depeursinge, A. (2008) · SERVAL (Université de Lausanne) · Cited by 4 · see citing works
Self-citationA framework for diagnosing interstitial lung diseases in HRCT : the TALISMAN project
Depeursinge, A. (2008) · SERVAL (Université de Lausanne) · Cited by 3 · see citing works
Self-citationA framework for diagnosis of interstitial lung diseases in HRCT: the TALISMAN project
Depeursinge, A., Iavindrasana, J., Andriambololoniaina, F. H., Poletti, P.-A., Platon, A., Geissbühler, A., & Müller, H. (2008) · Swiss Medical Informatics · doi:10.4414/smi.24.167 · Cited by 2 · see citing works
Self-citation
Flagged first, then by citations
2 of the 49 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
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 · see citing works
Greenspan, H. K., van Ginneken, B., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2553401 · Cited by 1,704 · see citing works
Lung pattern classification for interstitial lung diseases using a deep convolutional neural network
Anthimopoulos, M., Christodoulidis, S., Ebner, L., Christe, A., & Mougiakakou, S. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2535865 · Cited by 1,343 · see citing works
“The dataset used for training and evaluating the proposed method was made using two databases of ILD CT scans from two different Swiss university hospitals: • The first is the publicly available multimedia database of ILDs from the University Hospital of Geneva [30], which consists of 109 HRCT scans of different ILD c…”
MethodMedical image classification with convolutional neural network
Li, Q., Cai, W., Wang, X., Zhou, Y., Feng, D. D., & Chen, M. (2014) · 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV) · doi:10.1109/icarcv.2014.7064414 · Cited by 880 · see citing works
“Following [3], we chose to classify image patches of five ILD categories: normal (N), emphysema (E), ground glass (G), fibrosis (F) and micronodules (M).”
MethodHatabu, H., Hunninghake, G. M., Richeldi, L., Brown, K. M., Wells, A. U., Rémy‐Jardin, M., Verschakelen, J., Nicholson, A. G., Beasley, M. B., Christiani, D. C., et al. (2020) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(20)30168-5 · Cited by 600 · see citing works
Development and evaluation of an artificial intelligence system for COVID-19 diagnosis
Jin, C., Chen, W., Cao, Y., Xu, Z., Tan, Z., Zhang, X., Deng, L., Zheng, C., Zhou, J., Shi, H., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18685-1 · Cited by 591 · see citing works
Multisource Transfer Learning With Convolutional Neural Networks for Lung Pattern Analysis
Christodoulidis, S., Anthimopoulos, M., Ebner, L., Christe, A., & Mougiakakou, S. G. (2016) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2636929 · Cited by 296 · see citing works
“Dataset: The HUG database [39] consists of 109 HRCT scans of different ILD cases with 512 × 512 pixels per slice and an average of 25 slices per case.”
BackgroundInfluentialA Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)
Islam, M. M., Karray, F. O., Alhajj, R., & Zeng, J. (2021) · IEEE Access · doi:10.1109/access.2021.3058537 · Cited by 292 · see citing works
“[61] Three different hospitals (Wuhan Union Hospital, Western Campus of Wuhan Union Hospital, Jianghan Mobile Cabin Hospital), LIDC-IDRI [71], ILD-HUG [72] 1881 (COVID-19 positive=496, COVID-19 negative=1385) 2 (COVID-19 positive, COVID-19 negative) Random partition ResNet152 Accuracy=94.”
BackgroundGoing Deep in Medical Image Analysis: Concepts, Methods, Challenges, and Future Directions
Altaf, F., Islam, S. M. S., Akhtar, N., & Janjua, N. K. (2019) · IEEE Access · doi:10.1109/access.2019.2929365 · Cited by 293 · see citing works
“Their experiments used a public data [254] on which the classification accuracy improved to 87 .”
MethodGao, M., Bağcı, U., Lü, L., Wu, A., Buty, M., Shin, H.-C., Roth, H. R., Papadakis, G. Z., Depeursinge, A., Summers, R. M., et al. (2016) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2015.1124249 · Cited by 259 · see citing works
“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-citationFeature-Based Image Patch Approximation for Lung Tissue Classification
Song, Y., Cai, W., Zhou, Y., & Feng, D. D. (2013) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2013.2241448 · Cited by 177 · see citing works
Review on Diagnosis of COVID-19 from Chest CT Images Using Artificial Intelligence
Özşahin, İ., Şekeroğlu, B., Musa, M. S., Mustapha, M. T., & Ozsahin, D. U. (2020) · Computational and Mathematical Methods in Medicine · doi:10.1155/2020/9756518 · Cited by 179 · see citing works
“Besides, they used two publicly available international databases, LIDC-IDRI [28] and ILDHUG [31] (1012 and 113 subjects, respectively) as negative cases to develop the system.”
MethodChintakindi, B. M., Hashmi, M. F., Bokde, N. D., & Geem, Z. W. (2020) · Applied Sciences · doi:10.3390/app10093280 · Cited by 178 · see citing works
“ILD [188] and LIDC-IDRI [189] datasets for the lung; ADNI [190], BRATS [191], and OASIS-3 [175,192] datasets for the brain; DDSM [193], MIAS [194], CAMELYON 17 [195], and INbreast [196] datasets for the breast; DRIVE [197], STARE [198], and MESSIDOR-2 [199] datasets for the eye.”
BackgroundSemantic Segmentation of Pathological Lung Tissue With Dilated Fully Convolutional Networks
Anthimopoulos, M., Christodoulidis, S., Ebner, L., Geiser, T., Christe, A., & Mougiakakou, S. G. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2818620 · Cited by 152 · see citing works
“The dataset contains 109 cases from the publicly available multimedia database of interstitial lung diseases [35] by the Geneva University Hospital (HUG), along with 63 cases from Bern University Hospital - “Inselspital” (INSEL), as collected by the authors.”
MethodComputer-Aided Diagnosis of Pulmonary Fibrosis Using Deep Learning and CT Images
Christe, A., Peters, A. A., Drakopoulos, D., Heverhagen, J. T., Geiser, T., Stathopoulou, T., Christodoulidis, S., Anthimopoulos, M., Mougiakakou, S. G., & Ebner, L. (2019) · Investigative Radiology · doi:10.1097/rli.0000000000000574 · Cited by 149 · see citing works
Khanna, A., Londhe, N. D., Gupta, S., & Semwal, A. (2020) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2020.07.007 · Cited by 149 · see citing works
van Tulder, G., & de Bruijne, M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2526687 · Cited by 142 · see citing works
“vided by the University Hospitals of Geneva [8].”
MethodInfluentialUAV-Based Slope Failure Detection Using Deep-Learning Convolutional Neural Networks
Ghorbanzadeh, O., Meena, S. R., Blaschke, T., & Aryal, J. (2019) · Remote Sensing · doi:10.3390/rs11172046 · Cited by 130 · see citing works
“The ultimate bjective of preparing any CNN training dataset is to obtain a consistent set of sample patch s for feature extraction, segmentation, and object detectio [37].”
BackgroundHarrison, A. P., Xu, Z., George, K., Lu, L., Summers, R. M., & Mollura, D. J. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66179-7_71 · Cited by 129 · see citing works
“We measure PLS performance using multi-institutional data from the Lung Tissue Research Consortium (LTRC) ILD/COPD dataset [6], the University Hospitals of Geneva (UHG) ILD dataset [2], and a subset of an infection-based dataset from the National Institutes of Health (NIH) Clinical Center [9](2).”
MethodDeep learning: definition and perspectives for thoracic imaging
Chassagnon, G., Vakalopolou, M., Nikos Paragios, A., & Revel, M. (2019) · European Radiology · doi:10.1007/s00330-019-06564-3 · Cited by 116 · see citing works
Artificial intelligence in diagnosis of obstructive lung disease
Das, N., Topalovic, M., & Janssens, W. (2017) · Current Opinion in Pulmonary Medicine · doi:10.1097/mcp.0000000000000459 · Cited by 114 · see citing works
Capsules for biomedical image segmentation
LaLonde, R., Xu, Z., Irmakci, I., Jain, S., & Bağcı, U. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101889 · Cited by 110 · see citing works
“• The Multimedia Database of Interstitial Lung Diseases (Depeursinge et al., 2012), abbreviated as UHG, built at the University Hospitals of Geneva contains 214 annotated CT scans of patients affected with one of the 13 histological diagnoses of interstitial lung disease (ILD).”
InfluentialA review on the use of deep learning for medical images segmentation
Aljabri, M., & Alghamdi, M. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.07.070 · Cited by 109 · see citing works
Multiscale Rotation-Invariant Convolutional Neural Networks for Lung Texture Classification
Wang, Q., Zheng, Y., Yang, G., Jin, W., Chen, X., & Yin, Y. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2685586 · Cited by 104 · see citing works
“The data comes from the publicly available database of ILD cases [48] which contains HRCT images with a slice thickness of 1mm.”
BackgroundMethodDraelos, R. L., Dov, D., Mazurowski, M. A., Lo, J. Y.-C., Henao, R., Rubin, G. D., & Carin, L. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101857 · Cited by 97 · see citing works
“…al., 2019), interstitial lung disease (Anthimopoulos et al., 2016; Bermejo-Peláez et al., 2020; Christe et al., 2019; Christodoulidis et al., 2017; Depeursinge et al., 2012; Gao et al., 2018, 2016; Walsh et al., 2018; Wang et al., 2019), liver fibrosis (Choi et al., 2018), colon polyps (Nguyen et…”
BackgroundA holistic overview of deep learning approach in medical imaging
Yousef, R., Gupta, G., Yousef, N., & Khari, M. (2022) · Multimedia Systems · doi:10.1007/s00530-021-00884-5 · Cited by 105 · see citing works
“[229] Brain MRI 819 patients ChestX-ray14 [129] Lung X-ray 30,000 + patients LIDC-IDRI [230] Lung CT 1018 patients ILD [231] Lung CT 120 patients TCIA Multiple organs with cancer CT, MRI, PET 30.9 million images/37,568 patients DRIVE [232] Eye SLO 400 patients STARE [233] Eye It is used to…”
MethodInfluentialNear-Affine-Invariant Texture Learning for Lung Tissue Analysis Using Isotropic Wavelet Frames
Depeursinge, A., Ville, D. V. D., Platon, A., Geissbühler, A., Poletti, P., & Müller, H. (2012) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2012.2198829 · Cited by 83 · see citing works
Self-citationSoffer, S., Morgenthau, A. S., Shimon, O., Barash, Y., Konen, E., Glicksberg, B. S., & Klang, E. (2021) · Academic Radiology · doi:10.1016/j.acra.2021.05.014 · Cited by 84 · see citing works
The Role of Generative Adversarial Network in Medical Image Analysis: An In-depth Survey
Alamir, M., & Alghamdi, M. (2022) · ACM Computing Surveys · doi:10.1145/3527849 · Cited by 84 · see citing works
“ILD[45] Dicescore=0.9899 DecourtandDuong[42] [heart]Penalizingincorrectpredictionsbyusinglossfunctionrelyingondistancetransformandpixel-wisecross-entropy.”
InfluentialLarge 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 · see citing works
“The ILD database [62] has been developed by Depeursinge et al.”
MethodPang, S., Yu, Z., & Orgun, M. A. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2016.12.019 · Cited by 79 · see citing works
Learning to Read Chest X-Ray Images from 16000+ Examples Using CNN
Dong, Y. C., Pan, Y., Zhang, J., & Xu, W. (2017) · IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies · doi:10.1109/chase.2017.59 · Cited by 77 · see citing works
Ferré, E. M. N., Break, T. J., Burbelo, P. D., Allgäuer, M., Kleiner, D. E., Jin, D., Xu, Z., Folio, L. R., Mollura, D. J., Swamydas, M., et al. (2019) · Science Translational Medicine · doi:10.1126/scitranslmed.aav5597 · Cited by 75 · see citing works
Huang, S., Lee, F., Miao, R., Si, Q., Lu, C., & Chen, Q. (2020) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-019-02111-w · Cited by 72 · see citing works
Chassagnon, G., Vakalopoulou, M., Régent, A., Zacharaki, E. I., Aviram, G., Martin, C., Marini, R., Bus, N., Jerjir, N., MEKINIAN, A., et al. (2020) · Radiology Artificial Intelligence · doi:10.1148/ryai.2020190006 · Cited by 65 · see citing works
“Therefore, the goal of our study was to develop a robust deep learning–based quantification tool trained from a fully annotated dataset of contiguous CT images of the same ILD, allowing for automated quantification of ILD extent in patients with SSc. Adaptation of such a tool within the clinic could be used to help im…”
BackgroundShah, M. I., Mishra, S., Yadav, V. K., Chauhan, A., Sarkar, M. S., Sharma, S. K., & Rout, C. (2017) · Journal of medical imaging · doi:10.1117/1.jmi.4.2.027503 · Cited by 65 · see citing works
Deep Learning-Based Image Reconstruction for Different Medical Imaging Modalities
Yaqub, M., Feng, J., Arshid, K., Ahmed, S., Zhang, W., Nawaz, M. Z., & Mahmood, T. (2022) · Computational and Mathematical Methods in Medicine · doi:10.1155/2022/8750648 · Cited by 69 · see citing works
“[47] A U-net-based image reconstruction framework.”
Özyurt, F., Tuncer, T., & Subaşı, A. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104356 · Cited by 62 · see citing works
Unsupervised Deep Anomaly Detection for Medical Images Using an Improved Adversarial Autoencoder
Zhang, H., Guo, W., Zhang, S., Lu, H., & Zhao, X. (2022) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-021-00558-8 · Cited by 55 · see citing works
“ILD This dataset is the popular ILD database publicly available from the University Hospital of Geneva [19], including 109 High Resolution CT of different ILDs.”
MethodPang, T., Guo, S., Zhang, X., & Zhao, L. (2019) · BioMed Research International · doi:10.1155/2019/2045432 · Cited by 55 · see citing works
“We experimented on the ILD Database-MedGIFT [38] 4 BioMed Research International the ROI images here are cropped to pixels 512 × 512, of which 80% (1557) are training data and 20% (389) are testing data.”
MethodAbut, S., Okut, H., & Kallail, K. J. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122983 · Cited by 58 · see citing works
Optimizing Sample Patches Selection of CNN to Improve the mIOU on Landslide Detection
Ghorbanzadeh, O., & Blaschke, T. (2019) · International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM) · doi:10.5220/0007675300330040 · Cited by 51 · see citing works
“object detection or classification aims (Depeursinge et al., 2012).”
BackgroundLung image patch classification with automatic feature learning
Li, Q., Cai, W., & Feng, D. D. (2013) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2013.6610939 · Cited by 50 · see citing works
“The publicly available database of ILD cases [22] is used in this study.”
BackgroundMethodTwo‐Stage Hybrid Approach of Deep Learning Networks for Interstitial Lung Disease Classification
Pawar, S. P., & Talbar, S. N. (2022) · BioMed Research International · doi:10.1155/2022/7340902 · Cited by 51 · see citing works
“Performance analysis of the proposed ILD classi fi er has been demonstrated using an ILD database prepared by Depeursinge et al. [33].”
Lung Texture Classification Using Locally–Oriented Riesz Components
Depeursinge, A., Foncubierta–Rodríguez, A., Ville, D. V. D., & Müller, H. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23626-6_29 · Cited by 48 · see citing works
Self-citationClassification of interstitial lung disease patterns using local DCT features and random forest
Anthimopoulos, M., Christodoulidis, S., Christe, A., & Mougiakakou, S. G. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6945006 · Cited by 48 · see citing works
“For the needs of the present study, the publicly available TALISMAN database was used [19].”
MethodDeep learning for screening of interstitial lung disease patterns in high-resolution CT images
Agarwala, S., Kale, M., Kumar, D. K. S., Swaroop, R., Kumar, A., Dhara, A. K., Thakur, S. B., Sadhu, A. K., & Nandi, D. (2020) · Clinical Radiology · doi:10.1016/j.crad.2020.01.010 · Cited by 48 · see citing works
Comelli, A., Coronnello, C., Dahiya, N., Benfante, V., Palmucci, S., Basile, A., Vancheri, C., Russo, G. I., Yezzi, A. J., & Stefano, A. (2020) · Journal of Imaging · doi:10.3390/jimaging6110125 · Cited by 46 · see citing works
“In [41], an algorithm based on random forest, deep convolutional network, and multi-scale super-pixels was proposed for segmenting lungs with interstitial lung disease (IDL) using the ILDs database [42] with an average DSC of 96.”
MethodLungSeg-Net: Lung field segmentation using generative adversarial network
Pawar, S. P., & Talbar, S. N. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102296 · Cited by 45 · see citing works
Multiscale Lung Texture Signature Learning Using the Riesz Transform
Depeursinge, A., Foncubierta–Rodríguez, A., Ville, D. V. D., & Müller, H. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33454-2_64 · Cited by 44 · see citing works
“A publicly available dataset of 85 ILD cases with annotated HRCT images is used to evaluate our approach [12].”
MethodSelf-citationArtificial intelligence in radiology
Jin, D., Harrison, A. P., Zhang, L., Yan, K., Wang, Y., Cai, J., Miao, S., & Lü, L. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-821259-2.00014-4 · Cited by 48 · see citing works
“52 Nonetheless, further work is needed.”
A Survey of Medical Image Analysis Using Deep Learning Approaches
Rehman, A. O., Butt, M. A., & Zaman, M. (2021) · International Conference on Computing Methodologies and Communication (ICCMC) · doi:10.1109/iccmc51019.2021.9418385 · Cited by 42 · see citing works
“…et al [27] proposed a deep fully convolutional neural network for performing semantic segmentation of Interstitial Lung Diseases (ILD) [28] patterns which makes use of dilated convolutions with lung HRCT images as the input of the arbitrary size and produces the corresponding class…”
BackgroundInterstitial lung disease classification using improved DenseNet
Guo, W., Xu, Z., & Zhang, H. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6535-y · Cited by 40 · see citing works
Vishraj, R., Gupta, S., & Singh, S. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.108450 · Cited by 37 · see citing works
A Novel Denoising Algorithm Based on Wavelet and Non-Local Moment Mean Filtering
Liu, C., & Zhang, L. (2023) · Electronics · doi:10.3390/electronics12061461 · Cited by 37 · see citing works
Automatic lung segmentation based on image decomposition and wavelet transform
Liu, C., & Pang, M. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102032 · Cited by 37 · see citing works
Yoo, S., Yoon, S. H., Lee, J. H., Kim, K. H., Choi, H. I., Park, S. J., & Goo, J. M. (2020) · Korean Journal of Radiology · doi:10.3348/kjr.2020.0318 · Cited by 37 · see citing works
“For external validation, we used the University Hospitals of Geneva (HUG)-ILD dataset (15), which consisted of 109 annotated CT scans with advanced ILD.”
A fully automatic segmentation algorithm for CT lung images based on random forest
Liu, C., Zhao, R., & Pang, M. (2019) · Medical Physics · doi:10.1002/mp.13939 · Cited by 35 · see citing works
Gao, M., Xu, Z., Lu, L., Wu, A., Nogues, I., Summers, R. M., & Mollura, D. J. (2016) · 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2016.7493497 · Cited by 48 · see citing works
“2016 International Symposium on Biomedical Imaging References: [1] Depeursinge, Adrien, et al.”
BackgroundApplications of Machine Learning in Digital Forensics
Qadir, S., & Noor, B. (2021) · 2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) · doi:10.1109/icodt252288.2021.9441543 · Cited by 18 · see citing works
“[29] Computer aided detection of medical imaging tasks using CNN Image Forensics Supervised learning ✓ CNN For ILD dataset of [40] was used 905 patients”
MethodThree-stage segmentation of lung region from CT images using deep neural networks
Osadebey, M., Andersen, H. K., Waaler, D., Fossaa, K., Martinsen, A. C. T., & Pedersen, M. (2021) · BMC Medical Imaging · doi:10.1186/s12880-021-00640-1 · Cited by 34 · see citing works
Zhao, T., Gao, D., Wang, J., & Yin, Z. (2018) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2018.8363626 · Cited by 33 · see citing works
“The last dataset is a multimedia collection of cases with interstitial lung diseases from the University Hospitals of Geneva (HUG) [18].”
BackgroundDepeursinge, A., Vargas, A., Gaillard, F., Platon, A., Geissbühler, A., Poletti, P., & Müller, H. (2011) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-011-0618-9 · Cited by 33 · see citing works
“A multimedia library of ILD cases was built at the University Hospitals of Geneva (HUG) [ 7 ].”
MethodSelf-citationA comprehensive review of COVID-19 detection with machine learning and deep learning techniques
Das, S., Ayus, I., & Gupta, D. (2023) · Health and Technology · doi:10.1007/s12553-023-00757-z · Cited by 37 · see citing works
Lung Segmentation in CT Images: A Residual U-Net Approach on a Cross-Cohort Dataset
Sousa, J. V., Pereira, T., Silva, F., da Silva, M. C., Vilares, A. T., Cunha, A. M. T. D. S., & Oliveira, H. P. (2022) · Applied Sciences · doi:10.3390/app12041959 · Cited by 32 · see citing works
“University Hospitals of Geneva—Interstitial Lung Disease Motivated by the low availability of public collections of Interstitial Lung Disease (ILD) cases, the University Hospitals of Geneva Interstitial Lung Disease (HUG-ILD) database [21] was created to provide a public platform of interstitial lung disease (ILD)”
BackgroundRecent Advances of Artificial Intelligence Applications in Interstitial Lung Diseases
Exarchos, K., Gkrepi, G., Κostikas, Κ., & Gogali, A. (2023) · Diagnostics · doi:10.3390/diagnostics13132303 · Cited by 32 · see citing works
“• ILD Database from medGIFT [8]: a publicly available multimedia collection of ILD cases built from the University Hospitals of Geneva.”
BackgroundLocality-constrained Subcluster Representation Ensemble for lung image classification
Song, Y., Cai, W., Huang, H., Zhou, Y., Wang, Y., & Feng, D. D. (2015) · Medical Image Analysis · doi:10.1016/j.media.2015.03.003 · Cited by 31 · see citing works
“It is worth mentioning that the selection of the five tissue classes was motivated by the fact that they were the most common tissue patterns in ILDs (Depeursinge et al., 2012a) and the existing studies for this database focused on these five tissue classes.”
BackgroundMethodResultInfluentialUsing Multiscale Visual Words for Lung Texture Classification and Retrieval
Foncubierta–Rodríguez, A., Depeursinge, A., & Müller, H. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-28460-1_7 · Cited by 20 · see citing works
Self-citationLung segmentation based on random forest and multi‐scale edge detection
Liu, C., Zhao, R., & Pang, M. (2019) · IET Image Processing · doi:10.1049/iet-ipr.2019.0130 · Cited by 26 · see citing works
3D lung image retrieval using localized features
Depeursinge, A., Zrimec, T., Busayarat, S., & Müller, H. (2011) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.877943 · Cited by 26 · see citing works
Self-citationLearning Features for Tissue Classification with the Classification Restricted Boltzmann Machine
van Tulder, G., & de Bruijne, M. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-13972-2_5 · Cited by 25 · see citing works
“The second dataset is a larger, publicly available dataset on interstitial lung diseases (see [12] for a description).”
BackgroundFusing learned representations from Riesz Filters and Deep CNN for lung tissue classification
Joyseeree, R., Otálora, S., Müller, H., & Depeursinge, A. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.06.006 · Cited by 23 · see citing works
Self-citationAria, M., Nouranı, E., & Oskouei, A. G. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/2564022 · Cited by 24 · see citing works
“[74] Wuhan Union Hospital, LIDC-IDRI [88], ILD-HUG [89] Total: 1,881 COVID-19 : 496 Non-COVID-19 : 1,385 ResNet152 Accuracy: 94.”
Bergamasco, L. C. C., & Nunes, F. L. S. (2018) · Computer Science Review · doi:10.1016/j.cosrev.2018.10.003 · Cited by 23 · see citing works
Liu, C., Zhao, R., Xie, W., & Pang, M. (2020) · Neural Processing Letters · doi:10.1007/s11063-020-10330-8 · Cited by 22 · see citing works
“The images used for training and testing our algorithm come from ILDs database [39].”
MethodA Generalized Explanation Framework for Visualization of Deep Learning Model Predictions
Wang, P., & Vasconcelos, N. M. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3241106 · Cited by 21 · see citing works
“For example, when categorizing medical images with respect to interstitial lung diseases [90], [91], the AI system should explain a prediction of ‘emphysema’ by highlighting the regions of greatest uncertainty between this and alternative predictions, such as ‘normal’ or ‘fibrosis’.”
BackgroundGao, M., Xu, Z., Lü, L., Harrison, A. P., Summers, R. M., & Mollura, D. J. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-47157-0_18 · Cited by 21 · see citing works
“There are two main publicly available datasets for CT imaging based ILD classification [1, 2].”
BackgroundInfluentialSaha, S., Kumar, A., & Nandi, D. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.04.074 · Cited by 20 · see citing works
Interstitial Lung Disease Classification Using Feed Forward Neural Networks
Dudhane, A. A., Singadkar, G., Sanghavi, P. R., Jankharia, B. G., & Talbar, S. N. (2017) · doi:10.2991/iccasp-16.2017.75 · Cited by 20 · see citing works
“In this work, we have used publicly available dataset of ILD cases[1].”
BackgroundMethodLiu, C., & Pang, M. (2020) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-020-00388-0 · Cited by 20 · see citing works
“We test our algorithm on a set of experimental data from interstitial lung diseases (ILDs) database [27].”
MethodFeatures processing for random forest optimization in lung nodule localization
El-Askary, N. S., Salem, M. A. ‐., & Roushdy, M. I. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116489 · Cited by 20 · see citing works
Increasing CAD system efficacy for lung texture analysis using a convolutional network
Tarando, S. R., Fetita, C. I., Faccinetto, A., & Brillet, P. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2217752 · Cited by 20 · see citing works
“ailed in [7] (1 gny, France c are listed be ix 512x512; None; Axial”
BackgroundSchaer, R., Müller, H., & Depeursinge, A. (2016) · Journal of Imaging · doi:10.3390/jimaging2020019 · Cited by 20 · see citing works
“The medical image classification task used for this article consists of the identification of five lung texture patterns associated with interstitial lung diseases in high-resolution CT images [16].”
MethodSelf-citationMultiple Instance Learning in Medical Images: A Systematic Review
Barbosa, D., Ferreira, M. M., Bráz, G., Salgado, M., & Cunha, A. M. T. D. S. (2024) · IEEE Access · doi:10.1109/access.2024.3403538 · Cited by 18 · see citing works
3D Pyramid Pooling Network for Abdominal MRI Series Classification
Zhu, Z., Mittendorf, A., Shropshire, E., Allen, B., Miller, C., Bashir, M. R., & Mazurowski, M. A. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3033990 · Cited by 17 · see citing works
Lung CT Image Segmentation via Dilated U-Net Model and Multi-scale Gray Correlation-Based Approach
Liu, C., & Pang, M. (2023) · Circuits Systems and Signal Processing · doi:10.1007/s00034-023-02532-x · Cited by 17 · see citing works
“We test the proposed algorithm on a set of images from the multimedia database of interstitial lung diseases (ILDs) [12] and Lung Image Database Consortium image collection (LIDC-IDRI) data set [3].”
Lai, Y., Liu, X., Linning, E., Cheng, Y., Liu, S., Wu, Y., & Zheng, W. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124270 · Cited by 17 · see citing works
O’Neil, A. Q., Murchison, J. T., van Beek, E. J. R., & Goatman, K. A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67534-3_11 · Cited by 17 · see citing works
“Twenty computed tomography (CT) scan slices were selected from twenty different subjects in the MedGift ILD database [4].”
BackgroundMethodA Robust Feature Descriptor for Biomedical Image Retrieval
Das, P., & Neelima, A. (2020) · IRBM · doi:10.1016/j.irbm.2020.06.007 · Cited by 17 · see citing works
Copyright protection of multiple CT images using Octonion Krawtchouk moments and grey Wolf optimizer
Yamni, M., Daoui, A., Karmouni, H., Elmalih, S., Benfares, A., Sayyouri, M., Qjidaa, H., Maâroufi, M., Alami, B., & Jamil, M. O. (2023) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2023.03.008 · Cited by 17 · see citing works
Differential diagnosis of Interstitial Lung Diseases using Deep Learning networks
Doddavarapu, V. N. S., Kande, G. B., & Rao, B. P. (2020) · The Imaging Science Journal · doi:10.1080/13682199.2020.1781394 · Cited by 16 · see citing works
“In the dataset [3], 113 sets of image slices and AROIs of 1458 are given for classi fi cation.”
BackgroundMethodInfluentialPathology-centric medical image retrieval with hierarchical contextual spatial descriptor
Song, Y., Cai, W., Zhou, Y., Wen, L., & Feng, D. D. (2013) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2013.6556446 · Cited by 14 · see citing works
“lung cancer), the features designed can be quite generic [2] or adaptive to the disease [3, 4, 5].”
BackgroundBG-Net: Boundary-Guided Network for Lung Segmentation on Clinical CT Images
Xu, R., Wang, Y., Liu, T., Ye, X., Lin, L., Chen, Y., Kido, S., & Tomiyama, N. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412621 · Cited by 14 · see citing works
“For the HUG and VESSEL12 dataset, the method proposed in this paper obains the DSC of 98.89 % and 99.45 % respectively.”
MethodInfluentialClassification of Chest CT Lung Nodules Using Collaborative Deep Learning Model
Alshamrani, K. A., & Alshamrani, H. (2024) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s456167 · Cited by 14 · see citing works
Multiscale and Multidirectional Biomedical Texture Analysis
Depeursinge, A. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-812133-7.00002-8 · Cited by 18 · see citing works
“(F) Honeycombing fibrosis in lung CT [22].”
Self-citationDeep Learning for Medical Image Recognition: Open Issues and a Way to Forward
Nair, M. M., Kumari, S., Tyagi, A. K., & Sravanthi, K. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-15-9689-6_38 · Cited by 14 · see citing works
Agarwala, S., Kumar, A., Dhara, A. K., Thakur, S. B., Sadhu, A. K., & Nandi, D. (2021) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661821040027 · Cited by 14 · see citing works
“Publicly available MedGIFT database [8] is used for this study.”
MethodLung texture classification using bag of visual words
Asherov, M., Diamant, I., & Greenspan, H. K. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2044162 · Cited by 14 · see citing works
Aliboni, L., Dias, O. M., Pennati, F., Baldi, B. G., Sawamura, M. V. Y., Chate, R. C., de Carvalho, C. R. R., de Albuquerque, A. L. P., & Aliverti, A. (2020) · Academic Radiology · doi:10.1016/j.acra.2020.10.009 · Cited by 14 · see citing works
Extracting Lungs from CT Images Using Fully Convolutional Networks
Alves, J. H., Neto, P. M. M., & de Oliveira, L. F. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489223 · Cited by 13 · see citing works
“In the HUG-ILD, Fold #4 had the lowest mean and highest standard deviation be-tween all folds.”
Influential
- Authors
- Depeursinge, A., Vargas, A., Platon, A., Geissbuhler, A., Poletti, P.-A., & Müller, H.
- Affiliations
- HES-SO University of Applied Sciences and Arts Western Switzerland (CH)
- University Hospital of Geneva (CH)
- Corresponding author
- Depeursinge, Adrien
- Venue
- Computerized Medical Imaging and Graphics
- Publisher
- Elsevier BV
- Published
- 2012-04
- Volume
- 36
- Issue
- 3
- Pages
- 227-238
- ISSN
- 0730-4862 (ISSN-L)
- 0895-6111
- OpenAlex
- W2099707769
- Cited by
- 248
- References
- 49
- Citations by year
- 2026: 8 · 2025: 15 · 2024: 16 · 2023: 26 · 2022: 31 · 2021: 26 · 2020: 32 · 2019: 19 · 2018: 18 · 2017: 19 · 2016: 14 · 2015: 4 · 2014: 9 · 2013: 4 · 2012: 4
- FWCI
- 5.44
- Type
- journal article
- Language
- en
- Open access
- Licence
- Locations
- Computerized Medical Imaging and Graphics (publishedVersion)
- PubMed (publishedVersion)
- Archive ouverte UNIGE (University of Geneva) (submittedVersion, cc-by)
- SERVAL (Université de Lausanne) (submittedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, other-oa)
- Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- Topics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Keywords
- interstitial lung disease, multimedia databases, high-resolution computed tomography, clinical features, computer-aided diagnosis, lung histopathology
- MeSH terms
- Diagnosis, Computer-Assisted, Humans, Image Interpretation, Computer-Assisted, Tomography, X-Ray Computed, User-Computer Interface, Databases, Factual, Information Storage and Retrieval, Program Development, Lung Diseases, Interstitial, Multimedia
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
