1
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Zhang L, Shao Y, Chen G, Tian S, Zhang Q, Wu J, Bai C, Yang D. An artificial intelligence-assisted diagnostic system for the prediction of benignity and malignancy of pulmonary nodules and its practical value for patients with different clinical characteristics. Front Med (Lausanne) 2023; 10:1286433. [PMID: 38196835 PMCID: PMC10774219 DOI: 10.3389/fmed.2023.1286433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 12/12/2023] [Indexed: 01/11/2024] Open
Abstract
Objectives This study aimed to explore the value of an artificial intelligence (AI)-assisted diagnostic system in the prediction of pulmonary nodules. Methods The AI system was able to make predictions of benign or malignant nodules. 260 cases of solitary pulmonary nodules (SPNs) were divided into 173 malignant cases and 87 benign cases based on the surgical pathological diagnosis. A stratified data analysis was applied to compare the diagnostic effectiveness of the AI system to distinguish between the subgroups with different clinical characteristics. Results The accuracy of AI system in judging benignity and malignancy of the nodules was 75.77% (p < 0.05). We created an ROC curve by calculating the true positive rate (TPR) and the false positive rate (FPR) at different threshold values, and the AUC was 0.755. Results of the stratified analysis were as follows. (1) By nodule position: the AUC was 0.677, 0.758, 0.744, 0.982, and 0.725, respectively, for the nodules in the left upper lobe, left lower lobe, right upper lobe, right middle lobe, and right lower lobe. (2) By nodule size: the AUC was 0.778, 0.771, and 0.686, respectively, for the nodules measuring 5-10, 10-20, and 20-30 mm in diameter. (3) The predictive accuracy was higher for the subsolid pulmonary nodules than for the solid ones (80.54 vs. 66.67%). Conclusion The AI system can be applied to assist in the prediction of benign and malignant pulmonary nodules. It can provide a valuable reference, especially for the diagnosis of subsolid nodules and small nodules measuring 5-10 mm in diameter.
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Affiliation(s)
- Lichuan Zhang
- Department of Respiratory Medicine, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Yue Shao
- Department of Respiratory Medicine, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Guangmei Chen
- Department of Respiratory Medicine, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Simiao Tian
- Department of Respiratory Medicine, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Qing Zhang
- Department of Respiratory Medicine, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Jianlin Wu
- Department of Respiratory Medicine, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Chunxue Bai
- Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital Fudan University, Shanghai, China
- Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China
- Shanghai Respiratory Research Institution, Shanghai, China
| | - Dawei Yang
- Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital Fudan University, Shanghai, China
- Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China
- Shanghai Respiratory Research Institution, Shanghai, China
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2
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Wang L. Deep Learning Techniques to Diagnose Lung Cancer. Cancers (Basel) 2022; 14:cancers14225569. [PMID: 36428662 PMCID: PMC9688236 DOI: 10.3390/cancers14225569] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 11/11/2022] [Accepted: 11/11/2022] [Indexed: 11/15/2022] Open
Abstract
Medical imaging tools are essential in early-stage lung cancer diagnostics and the monitoring of lung cancer during treatment. Various medical imaging modalities, such as chest X-ray, magnetic resonance imaging, positron emission tomography, computed tomography, and molecular imaging techniques, have been extensively studied for lung cancer detection. These techniques have some limitations, including not classifying cancer images automatically, which is unsuitable for patients with other pathologies. It is urgently necessary to develop a sensitive and accurate approach to the early diagnosis of lung cancer. Deep learning is one of the fastest-growing topics in medical imaging, with rapidly emerging applications spanning medical image-based and textural data modalities. With the help of deep learning-based medical imaging tools, clinicians can detect and classify lung nodules more accurately and quickly. This paper presents the recent development of deep learning-based imaging techniques for early lung cancer detection.
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Affiliation(s)
- Lulu Wang
- Biomedical Device Innovation Center, Shenzhen Technology University, Shenzhen 518118, China
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3
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杨 杨. Advances in the Classification of Benign and Malignant Pulmonary Nodules Based on Machine Learning. Biophysics (Nagoya-shi) 2021. [DOI: 10.12677/biphy.2021.92006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
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4
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Perez G, Arbelaez P. Automated lung cancer diagnosis using three-dimensional convolutional neural networks. Med Biol Eng Comput 2020; 58:1803-1815. [PMID: 32504345 DOI: 10.1007/s11517-020-02197-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Accepted: 05/20/2020] [Indexed: 11/24/2022]
Abstract
Lung cancer is the deadliest cancer worldwide. It has been shown that early detection using low-dose computer tomography (LDCT) scans can reduce deaths caused by this disease. We present a general framework for the detection of lung cancer in chest LDCT images. Our method consists of a nodule detector trained on the LIDC-IDRI dataset followed by a cancer predictor trained on the Kaggle DSB 2017 dataset and evaluated on the IEEE International Symposium on Biomedical Imaging (ISBI) 2018 Lung Nodule Malignancy Prediction test set. Our candidate extraction approach is effective to produce accurate candidates with a recall of 99.6%. In addition, our false positive reduction stage classifies successfully the candidates and increases precision by a factor of 2000. Our cancer predictor obtained a ROC AUC of 0.913 and was ranked 1st place at the ISBI 2018 Lung Nodule Malignancy Prediction challenge. Graphical abstract.
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Affiliation(s)
- Gustavo Perez
- Universidad de los Andes, Cra 1 N 18A-12, Bogota, 111711, Colombia.
| | - Pablo Arbelaez
- Universidad de los Andes, Cra 1 N 18A-12, Bogota, 111711, Colombia
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5
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Kauczor HU, Baird AM, Blum TG, Bonomo L, Bostantzoglou C, Burghuber O, Čepická B, Comanescu A, Couraud S, Devaraj A, Jespersen V, Morozov S, Nardi Agmon I, Peled N, Powell P, Prosch H, Ravara S, Rawlinson J, Revel MP, Silva M, Snoeckx A, van Ginneken B, van Meerbeeck JP, Vardavas C, von Stackelberg O, Gaga M. ESR/ERS statement paper on lung cancer screening. Eur Respir J 2020; 55:13993003.00506-2019. [PMID: 32051182 DOI: 10.1183/13993003.00506-2019] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Accepted: 08/16/2019] [Indexed: 12/18/2022]
Abstract
In Europe, lung cancer ranks third among the most common cancers, remaining the biggest killer. Since the publication of the first European Society of Radiology and European Respiratory Society joint white paper on lung cancer screening (LCS) in 2015, many new findings have been published and discussions have increased considerably. Thus, this updated expert opinion represents a narrative, non-systematic review of the evidence from LCS trials and description of the current practice of LCS as well as aspects that have not received adequate attention until now. Reaching out to the potential participants (persons at high risk), optimal communication and shared decision-making will be key starting points. Furthermore, standards for infrastructure, pathways and quality assurance are pivotal, including promoting tobacco cessation, benefits and harms, overdiagnosis, quality, minimum radiation exposure, definition of management of positive screen results and incidental findings linked to respective actions as well as cost-effectiveness. This requires a multidisciplinary team with experts from pulmonology and radiology as well as thoracic oncologists, thoracic surgeons, pathologists, family doctors, patient representatives and others. The ESR and ERS agree that Europe's health systems need to adapt to allow citizens to benefit from organised pathways, rather than unsupervised initiatives, to allow early diagnosis of lung cancer and reduce the mortality rate. Now is the time to set up and conduct demonstration programmes focusing, among other points, on methodology, standardisation, tobacco cessation, education on healthy lifestyle, cost-effectiveness and a central registry.
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Affiliation(s)
- Hans-Ulrich Kauczor
- Dept of Diagnostic and Interventional Radiology, University Hospital Heidelberg, German Center of Lung Research, Heidelberg, Germany
| | - Anne-Marie Baird
- Central Pathology Laboratory, Trinity College Dublin, St. James's Hospital, Dublin, Ireland
| | | | - Lorenzo Bonomo
- Dept of Radiology, Policlinico Universitario Agostino Gemelli, Rome, Italy
| | | | | | | | | | - Sébastien Couraud
- Service de Pneumologie et Oncologie Thoracique, Hospices Civils de Lyon, CH Lyon Sud, Pierre Bénite, France.,Faculté de Médecine et de Maïeutique Lyon Sud - Charles Mérieux, Université Claude Bernard Lyon I, Oullins, France
| | | | | | - Sergey Morozov
- Dept of Health Care of Moscow, Research and Practical Clinical Center of Diagnostics and Telemedicine Technologies, Moscow, Russian Federation
| | | | - Nir Peled
- Thoracic Cancer Unit, Rabin Medical Center, Petach Tiqwa, Israel
| | | | - Helmut Prosch
- Dept of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Sofia Ravara
- Medical Sciences, Faculty of Health Sciences, University of Beira Interior, Covilha, Portugal.,Tobacco Cessation Unit, CHCB University Hospital, Covilha, Portugal
| | | | | | - Mario Silva
- Section of Radiology, Dept of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | | | - Bram van Ginneken
- Image Sciences Institute, University Medical Centre, Utrecht, The Netherlands.,Dept of Radiology, Nijmegen Medical Centre, Nijmegen, The Netherlands
| | | | - Constantine Vardavas
- Clinic of Social and Family Medicine, Faculty of Medicine, University of Crete, Heraklion, Greece.,Center for Global Tobacco Control, Department of Society, Human Development and Health, Harvard School of Public Health, Boston, MA, USA
| | - Oyunbileg von Stackelberg
- Dept of Diagnostic and Interventional Radiology, University Hospital Heidelberg, German Center of Lung Research, Heidelberg, Germany
| | - Mina Gaga
- 7th Respiratory Medicine Dept, Athens Chest Hospital Sotiria, Athens, Greece
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6
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Kauczor HU, Baird AM, Blum TG, Bonomo L, Bostantzoglou C, Burghuber O, Čepická B, Comanescu A, Couraud S, Devaraj A, Jespersen V, Morozov S, Agmon IN, Peled N, Powell P, Prosch H, Ravara S, Rawlinson J, Revel MP, Silva M, Snoeckx A, van Ginneken B, van Meerbeeck JP, Vardavas C, von Stackelberg O, Gaga M. ESR/ERS statement paper on lung cancer screening. Eur Radiol 2020; 30:3277-3294. [PMID: 32052170 DOI: 10.1007/s00330-020-06727-7] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Accepted: 08/16/2019] [Indexed: 12/17/2022]
Abstract
In Europe, lung cancer ranks third among the most common cancers, remaining the biggest killer. Since the publication of the first European Society of Radiology and European Respiratory Society joint white paper on lung cancer screening (LCS) in 2015, many new findings have been published and discussions have increased considerably. Thus, this updated expert opinion represents a narrative, non-systematic review of the evidence from LCS trials and description of the current practice of LCS as well as aspects that have not received adequate attention until now. Reaching out to the potential participants (persons at high risk), optimal communication and shared decision-making will be key starting points. Furthermore, standards for infrastructure, pathways and quality assurance are pivotal, including promoting tobacco cessation, benefits and harms, overdiagnosis, quality, minimum radiation exposure, definition of management of positive screen results and incidental findings linked to respective actions as well as cost-effectiveness. This requires a multidisciplinary team with experts from pulmonology and radiology as well as thoracic oncologists, thoracic surgeons, pathologists, family doctors, patient representatives and others. The ESR and ERS agree that Europe's health systems need to adapt to allow citizens to benefit from organised pathways, rather than unsupervised initiatives, to allow early diagnosis of lung cancer and reduce the mortality rate. Now is the time to set up and conduct demonstration programmes focusing, among other points, on methodology, standardisation, tobacco cessation, education on healthy lifestyle, cost-effectiveness and a central registry.Key Points• Pulmonologists and radiologists both have key roles in the set up of multidisciplinary LCS teams with experts from many other fields.• Pulmonologists identify people eligible for LCS, reach out to family doctors, share the decision-making process and promote tobacco cessation.• Radiologists ensure appropriate image quality, minimum dose and a standardised reading/reporting algorithm, together with a clear definition of a "positive screen".• Strict algorithms define the exact management of screen-detected nodules and incidental findings.• For LCS to be (cost-)effective, it has to target a population defined by risk prediction models.
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Affiliation(s)
- Hans-Ulrich Kauczor
- Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, German Center of Lung Research, INF 110, 69120, Heidelberg, Germany.
| | - Anne-Marie Baird
- Central Pathology Laboratory, Trinity College Dublin, St. James's Hospital, Dublin, Ireland
| | | | - Lorenzo Bonomo
- Department of Radiology, Policlinico Universitario Agostino Gemelli, Rome, Italy
| | | | | | | | | | - Sébastien Couraud
- Service de Pneumologie et Oncologie Thoracique, Hospices Civils de Lyon, Sud, Pierre Bénite, Lyon, CH, France.,Faculté de Médecine et de Maïeutique Lyon Sud - Charles Mérieux, Université Claude Bernard Lyon I, Oullins, France
| | | | | | - Sergey Morozov
- Department of Health Care of Moscow, Research and Practical Clinical Center of Diagnostics and Telemedicine Technologies, Moscow, Russian Federation
| | | | - Nir Peled
- Thoracic Cancer Unit, Rabin Medical Center, Petach Tiqwa, Israel
| | | | - Helmut Prosch
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Sofia Ravara
- Medical Sciences, Faculty of Health Sciences, University of Beira Interior, Covilha, Portugal.,Tobacco Cessation Unit, CHCB University Hospital, Covilha, Portugal
| | | | | | - Mario Silva
- Section of Radiology, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | | | - Bram van Ginneken
- Image Sciences Institute, University Medical Centre, Utrecht, The Netherlands.,Department of Radiology, Nijmegen Medical Centre, Nijmegen, The Netherlands
| | | | - Constantine Vardavas
- Clinic of Social and Family Medicine, Faculty of Medicine, University of Crete, Heraklion, Greece.,Center for Global Tobacco Control, Department of Society, Human Development and Health, Harvard School of Public Health, Boston, MA, USA
| | - Oyunbileg von Stackelberg
- Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, German Center of Lung Research, INF 110, 69120, Heidelberg, Germany
| | - Mina Gaga
- 7th Respiratory Medicine Department, Athens Chest Hospital Sotiria, Athens, Greece
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7
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Oh J, Oh BL, Lee KU, Chae JH, Yun K. Identifying Schizophrenia Using Structural MRI With a Deep Learning Algorithm. Front Psychiatry 2020; 11:16. [PMID: 32116837 PMCID: PMC7008229 DOI: 10.3389/fpsyt.2020.00016] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 01/08/2020] [Indexed: 12/31/2022] Open
Abstract
OBJECTIVE Although distinctive structural abnormalities occur in patients with schizophrenia, detecting schizophrenia with magnetic resonance imaging (MRI) remains challenging. This study aimed to detect schizophrenia in structural MRI data sets using a trained deep learning algorithm. METHOD Five public MRI data sets (BrainGluSchi, COBRE, MCICShare, NMorphCH, and NUSDAST) from schizophrenia patients and normal subjects, for a total of 873 structural MRI data sets, were used to train a deep convolutional neural network. RESULTS The deep learning algorithm trained with structural MR images detected schizophrenia in randomly selected images with reliable performance (area under the receiver operating characteristic curve [AUC] of 0.96). The algorithm could also identify MR images from schizophrenia patients in a previously unencountered data set with an AUC of 0.71 to 0.90. The deep learning algorithm's classification performance degraded to an AUC of 0.71 when a new data set with younger patients and a shorter duration of illness than the training data sets was presented. The brain region contributing the most to the performance of the algorithm was the right temporal area, followed by the right parietal area. Semitrained clinical specialists hardly discriminated schizophrenia patients from healthy controls (AUC: 0.61) in the set of 100 randomly selected brain images. CONCLUSIONS The deep learning algorithm showed good performance in detecting schizophrenia and identified relevant structural features from structural brain MRI data; it had an acceptable classification performance in a separate group of patients at an earlier stage of the disease. Deep learning can be used to delineate the structural characteristics of schizophrenia and to provide supplementary diagnostic information in clinical settings.
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Affiliation(s)
- Jihoon Oh
- Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Baek-Lok Oh
- Department of Ophthalmology, Seoul National University Hospital, Seoul, South Korea
| | - Kyong-Uk Lee
- Department of Psychiatry, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Jeong-Ho Chae
- Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Kyongsik Yun
- Computation and Neural Systems, California Institute of Technology, Pasadena, CA, United States.,Bio-Inspired Technologies and Systems, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States
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8
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Kim TJ, Kim CH, Lee HY, Chung MJ, Shin SH, Lee KJ, Lee KS. Management of incidental pulmonary nodules: current strategies and future perspectives. Expert Rev Respir Med 2019; 14:173-194. [PMID: 31762330 DOI: 10.1080/17476348.2020.1697853] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Introduction: Detection and characterization of pulmonary nodules is an important issue, because the process is the first step in the management of lung cancers.Areas covered: Literature review was performed on May 15 2019 by using the PubMed, US National Library of Medicine National Institutes of Health, and the National Center for Biotechnology information. CT features helping identify the druggable mutations and predict the prognosis of malignant nodules were presented. Technical advancements in MRI and PET/CT were introduced for providing functional information about malignant nodules. Advances in various tissue biopsy techniques enabling molecular analysis and histologic diagnosis of indeterminate nodules were also presented. New techniques such as radiomics, deep learning (DL) technology, and artificial intelligence showing promise in differentiating between malignant and benign nodules were summarized. Recently, updated management guidelines for solid and subsolid nodules incidentally detected on CT were described. Risk stratification and prediction models for indeterminate nodules under active investigation were briefly summarized.Expert opinion: Advancement in CT knowledge has led to a better correlation between CT features and genomic alterations or tumor histology. Recent advances like PET/CT, MRI, radiomics, and DL-based approach have shown promising results in the characterization and prognostication of pulmonary nodules.
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Affiliation(s)
- Tae Jung Kim
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine (SKKU-SOM), Seoul, South Korea
| | - Cho Hee Kim
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine (SKKU-SOM), Seoul, South Korea
| | - Ho Yun Lee
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine (SKKU-SOM), Seoul, South Korea
| | - Myung Jin Chung
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine (SKKU-SOM), Seoul, South Korea
| | - Sun Hye Shin
- Respiratory and Critical Care Division of Department of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine (SKKU-SOM), Seoul, South Korea
| | - Kyung Jong Lee
- Respiratory and Critical Care Division of Department of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine (SKKU-SOM), Seoul, South Korea
| | - Kyung Soo Lee
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine (SKKU-SOM), Seoul, South Korea
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9
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Choudhary P, Hazra A. Chest disease radiography in twofold: using convolutional neural networks and transfer learning. EVOLVING SYSTEMS 2019. [DOI: 10.1007/s12530-019-09316-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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10
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11
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Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JAWM, van Ginneken B, Sánchez CI. A survey on deep learning in medical image analysis. Med Image Anal 2017; 42:60-88. [PMID: 28778026 DOI: 10.1016/j.media.2017.07.005] [Citation(s) in RCA: 4240] [Impact Index Per Article: 605.7] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Revised: 07/24/2017] [Accepted: 07/25/2017] [Indexed: 02/07/2023]
Affiliation(s)
- Geert Litjens
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands.
| | - Thijs Kooi
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | | | - Francesco Ciompi
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Mohsen Ghafoorian
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Bram van Ginneken
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Clara I Sánchez
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands
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12
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van Ginneken B. Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning. Radiol Phys Technol 2017; 10:23-32. [PMID: 28211015 PMCID: PMC5337239 DOI: 10.1007/s12194-017-0394-5] [Citation(s) in RCA: 71] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2017] [Accepted: 02/08/2017] [Indexed: 02/06/2023]
Abstract
Half a century ago, the term "computer-aided diagnosis" (CAD) was introduced in the scientific literature. Pulmonary imaging, with chest radiography and computed tomography, has always been one of the focus areas in this field. In this study, I describe how machine learning became the dominant technology for tackling CAD in the lungs, generally producing better results than do classical rule-based approaches, and how the field is now rapidly changing: in the last few years, we have seen how even better results can be obtained with deep learning. The key differences among rule-based processing, machine learning, and deep learning are summarized and illustrated for various applications of CAD in the chest.
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Affiliation(s)
- Bram van Ginneken
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands.
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