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Ou Z, Wang H, Zhang B, Liang H, Hu B, Ren L, Liu Y, Zhang Y, Dai C, Wu H, Li W, Li X. Early identification of stroke through deep learning with multi-modal human speech and movement data. Neural Regen Res 2025; 20:234-241. [PMID: 38767488 PMCID: PMC11246124 DOI: 10.4103/1673-5374.393103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 11/21/2023] [Indexed: 05/22/2024] Open
Abstract
JOURNAL/nrgr/04.03/01300535-202501000-00031/figure1/v/2024-05-14T021156Z/r/image-tiff Early identification and treatment of stroke can greatly improve patient outcomes and quality of life. Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale (CPSS) and the Face Arm Speech Test (FAST) are commonly used for stroke screening, accurate administration is dependent on specialized training. In this study, we proposed a novel multimodal deep learning approach, based on the FAST, for assessing suspected stroke patients exhibiting symptoms such as limb weakness, facial paresis, and speech disorders in acute settings. We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements, facial expressions, and speech tests based on the FAST. We compared the constructed deep learning model, which was designed to process multi-modal datasets, with six prior models that achieved good action classification performance, including the I3D, SlowFast, X3D, TPN, TimeSformer, and MViT. We found that the findings of our deep learning model had a higher clinical value compared with the other approaches. Moreover, the multi-modal model outperformed its single-module variants, highlighting the benefit of utilizing multiple types of patient data, such as action videos and speech audio. These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke, thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.
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Affiliation(s)
- Zijun Ou
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Haitao Wang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Bin Zhang
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
| | - Haobang Liang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Bei Hu
- Department of Emergency Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
| | - Longlong Ren
- Department of Emergency Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
| | - Yanjuan Liu
- Department of Emergency Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
| | - Yuhu Zhang
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
| | - Chengbo Dai
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
| | - Hejun Wu
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Weifeng Li
- Department of Emergency Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
| | - Xin Li
- Department of Emergency Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
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2
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Liu P, Ji L, Ye F, Fu B. AdvMIL: Adversarial multiple instance learning for the survival analysis on whole-slide images. Med Image Anal 2024; 91:103020. [PMID: 37926034 DOI: 10.1016/j.media.2023.103020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 04/05/2023] [Accepted: 10/31/2023] [Indexed: 11/07/2023]
Abstract
The survival analysis on histological whole-slide images (WSIs) is one of the most important means to estimate patient prognosis. Although many weakly-supervised deep learning models have been developed for gigapixel WSIs, their potential is generally restricted by classical survival analysis rules and fully-supervised learning requirements. As a result, these models provide patients only with a completely-certain point estimation of time-to-event, and they could only learn from the labeled WSI data currently at a small scale. To tackle these problems, we propose a novel adversarial multiple instance learning (AdvMIL) framework. This framework is based on adversarial time-to-event modeling, and integrates the multiple instance learning (MIL) that is much necessary for WSI representation learning. It is a plug-and-play one, so that most existing MIL-based end-to-end methods can be easily upgraded by applying this framework, gaining the improved abilities of survival distribution estimation and semi-supervised learning. Our extensive experiments show that AdvMIL not only could often bring performance improvement to mainstream WSI survival analysis methods at a relatively low computational cost, but also enables these methods to effectively utilize unlabeled data via semi-supervised learning. Moreover, it is observed that AdvMIL could help improving the robustness of models against patch occlusion and two representative image noises. The proposed AdvMIL framework could promote the research of survival analysis in computational pathology with its novel adversarial MIL paradigm.
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Affiliation(s)
- Pei Liu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Xiyuan Ave, Chengdu, 611731, Sichuan, China.
| | - Luping Ji
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Xiyuan Ave, Chengdu, 611731, Sichuan, China.
| | - Feng Ye
- Institute of Clinical Pathology, West China Hospital, Sichuan University, Guo Xue Xiang, Chengdu, 610041, Sichuan, China.
| | - Bo Fu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Xiyuan Ave, Chengdu, 611731, Sichuan, China.
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Xu Y, Zheng X, Li Y, Ye X, Cheng H, Wang H, Lyu J. Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review. J Evid Based Med 2023; 16:342-375. [PMID: 37718729 DOI: 10.1111/jebm.12548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 08/30/2023] [Indexed: 09/19/2023]
Abstract
BACKGROUND Increasingly, patient medication adherence data are being consolidated from claims databases and electronic health records (EHRs). Such databases offer an indirect avenue to gauge medication adherence in our data-rich healthcare milieu. The surge in data accessibility, coupled with the pressing need for its conversion to actionable insights, has spotlighted data mining, with machine learning (ML) emerging as a pivotal technique. Nonadherence poses heightened health risks and escalates medical costs. This paper elucidates the synergistic interaction between medical database mining for medication adherence and the role of ML in fostering knowledge discovery. METHODS We conducted a comprehensive review of EHR applications in the realm of medication adherence, leveraging ML techniques. We expounded on the evolution and structure of medical databases pertinent to medication adherence and harnessed both supervised and unsupervised ML paradigms to delve into adherence and its ramifications. RESULTS Our study underscores the applications of medical databases and ML, encompassing both supervised and unsupervised learning, for medication adherence in clinical big data. Databases like SEER and NHANES, often underutilized due to their intricacies, have gained prominence. Employing ML to excavate patient medication logs from these databases facilitates adherence analysis. Such findings are pivotal for clinical decision-making, risk stratification, and scholarly pursuits, aiming to elevate healthcare quality. CONCLUSION Advanced data mining in the era of big data has revolutionized medication adherence research, thereby enhancing patient care. Emphasizing bespoke interventions and research could herald transformative shifts in therapeutic modalities.
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Affiliation(s)
- Yixian Xu
- Department of Anesthesiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Xinkai Zheng
- Department of Dermatology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Yuanjie Li
- Planning & Discipline Construction Office, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Xinmiao Ye
- Department of Anesthesiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Hongtao Cheng
- School of Nursing, Jinan University, Guangzhou, China
| | - Hao Wang
- Department of Anesthesiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Jun Lyu
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Guangzhou, China
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Meng Y, Bridge J, Addison C, Wang M, Merritt C, Franks S, Mackey M, Messenger S, Sun R, Fitzmaurice T, McCann C, Li Q, Zhao Y, Zheng Y. Bilateral adaptive graph convolutional network on CT based Covid-19 diagnosis with uncertainty-aware consensus-assisted multiple instance learning. Med Image Anal 2023; 84:102722. [PMID: 36574737 PMCID: PMC9753459 DOI: 10.1016/j.media.2022.102722] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 10/17/2022] [Accepted: 12/02/2022] [Indexed: 12/23/2022]
Abstract
Coronavirus disease (COVID-19) has caused a worldwide pandemic, putting millions of people's health and lives in jeopardy. Detecting infected patients early on chest computed tomography (CT) is critical in combating COVID-19. Harnessing uncertainty-aware consensus-assisted multiple instance learning (UC-MIL), we propose to diagnose COVID-19 using a new bilateral adaptive graph-based (BA-GCN) model that can use both 2D and 3D discriminative information in 3D CT volumes with arbitrary number of slices. Given the importance of lung segmentation for this task, we have created the largest manual annotation dataset so far with 7,768 slices from COVID-19 patients, and have used it to train a 2D segmentation model to segment the lungs from individual slices and mask the lungs as the regions of interest for the subsequent analyses. We then used the UC-MIL model to estimate the uncertainty of each prediction and the consensus between multiple predictions on each CT slice to automatically select a fixed number of CT slices with reliable predictions for the subsequent model reasoning. Finally, we adaptively constructed a BA-GCN with vertices from different granularity levels (2D and 3D) to aggregate multi-level features for the final diagnosis with the benefits of the graph convolution network's superiority to tackle cross-granularity relationships. Experimental results on three largest COVID-19 CT datasets demonstrated that our model can produce reliable and accurate COVID-19 predictions using CT volumes with any number of slices, which outperforms existing approaches in terms of learning and generalisation ability. To promote reproducible research, we have made the datasets, including the manual annotations and cleaned CT dataset, as well as the implementation code, available at https://doi.org/10.5281/zenodo.6361963.
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Affiliation(s)
- Yanda Meng
- Department of Eye and Vision Science, University of Liverpool, Liverpool, United Kingdom
| | - Joshua Bridge
- Department of Eye and Vision Science, University of Liverpool, Liverpool, United Kingdom
| | - Cliff Addison
- Advanced Research Computing, University of Liverpool, Liverpool, United Kingdom
| | - Manhui Wang
- Advanced Research Computing, University of Liverpool, Liverpool, United Kingdom
| | | | - Stu Franks
- Alces Flight Limited, Bicester, United Kingdom
| | - Maria Mackey
- Amazon Web Services, 60 Holborn Viaduct, London, United Kingdom
| | - Steve Messenger
- Amazon Web Services, 60 Holborn Viaduct, London, United Kingdom
| | - Renrong Sun
- Department of Radiology, Hubei Provincial Hospital of Integrated Chinese and Western Medicine, Hubei University of Chinese Medicine, Wuhan, China
| | - Thomas Fitzmaurice
- Adult Cystic Fibrosis Unit, Liverpool Heart and Chest Hospital NHS Foundation Trust, Liverpool, United Kingdom
| | - Caroline McCann
- Radiology, Liverpool Heart and Chest Hospital NHS Foundation Trust, United Kingdom
| | - Qiang Li
- The Affiliated People’s Hospital of Ningbo University, Ningbo, China
| | - Yitian Zhao
- The Affiliated People's Hospital of Ningbo University, Ningbo, China; Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Science, Ningbo, China.
| | - Yalin Zheng
- Department of Eye and Vision Science, University of Liverpool, Liverpool, United Kingdom; Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom.
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Sherwani MK, Marzullo A, De Momi E, Calimeri F. Lesion segmentation in lung CT scans using unsupervised adversarial learning. Med Biol Eng Comput 2022; 60:3203-3215. [PMID: 36125656 PMCID: PMC9486778 DOI: 10.1007/s11517-022-02651-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 07/28/2022] [Indexed: 12/01/2022]
Abstract
Lesion segmentation in medical images is difficult yet crucial for proper diagnosis and treatment. Identifying lesions in medical images is costly and time-consuming and requires highly specialized knowledge. For this reason, supervised and semi-supervised learning techniques have been developed. Nevertheless, the lack of annotated data, which is common in medical imaging, is an issue; in this context, interesting approaches can use unsupervised learning to accurately distinguish between healthy tissues and lesions, training the network without using the annotations. In this work, an unsupervised learning technique is proposed to automatically segment coronavirus disease 2019 (COVID-19) lesions on 2D axial CT lung slices. The proposed approach uses the technique of image translation to generate healthy lung images based on the infected lung image without the need for lesion annotations. Attention masks are used to improve the quality of the segmentation further. Experiments showed the capability of the proposed approaches to segment the lesions, and it outperforms a range of unsupervised lesion detection approaches. The average reported results for the test dataset based on the metrics: Dice Score, Sensitivity, Specificity, Structure Measure, Enhanced-Alignment Measure, and Mean Absolute Error are 0.695, 0.694, 0.961, 0.791, 0.875, and 0.082 respectively. The achieved results are promising compared with the state-of-the-art and could constitute a valuable tool for future developments.
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Affiliation(s)
- Moiz Khan Sherwani
- Department of Mathematics and Computer Science, University of Calabria, Rende, Italy.
| | - Aldo Marzullo
- Department of Mathematics and Computer Science, University of Calabria, Rende, Italy
| | - Elena De Momi
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| | - Francesco Calimeri
- Department of Mathematics and Computer Science, University of Calabria, Rende, Italy
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Ali H, Shah Z. Combating COVID-19 Using Generative Adversarial Networks and Artificial Intelligence for Medical Images: Scoping Review. JMIR Med Inform 2022; 10:e37365. [PMID: 35709336 PMCID: PMC9246088 DOI: 10.2196/37365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 03/06/2022] [Accepted: 03/11/2022] [Indexed: 12/02/2022] Open
Abstract
Background Research on the diagnosis of COVID-19 using lung images is limited by the scarcity of imaging data. Generative adversarial networks (GANs) are popular for synthesis and data augmentation. GANs have been explored for data augmentation to enhance the performance of artificial intelligence (AI) methods for the diagnosis of COVID-19 within lung computed tomography (CT) and X-ray images. However, the role of GANs in overcoming data scarcity for COVID-19 is not well understood. Objective This review presents a comprehensive study on the role of GANs in addressing the challenges related to COVID-19 data scarcity and diagnosis. It is the first review that summarizes different GAN methods and lung imaging data sets for COVID-19. It attempts to answer the questions related to applications of GANs, popular GAN architectures, frequently used image modalities, and the availability of source code. Methods A search was conducted on 5 databases, namely PubMed, IEEEXplore, Association for Computing Machinery (ACM) Digital Library, Scopus, and Google Scholar. The search was conducted from October 11-13, 2021. The search was conducted using intervention keywords, such as “generative adversarial networks” and “GANs,” and application keywords, such as “COVID-19” and “coronavirus.” The review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines for systematic and scoping reviews. Only those studies were included that reported GAN-based methods for analyzing chest X-ray images, chest CT images, and chest ultrasound images. Any studies that used deep learning methods but did not use GANs were excluded. No restrictions were imposed on the country of publication, study design, or outcomes. Only those studies that were in English and were published from 2020 to 2022 were included. No studies before 2020 were included. Results This review included 57 full-text studies that reported the use of GANs for different applications in COVID-19 lung imaging data. Most of the studies (n=42, 74%) used GANs for data augmentation to enhance the performance of AI techniques for COVID-19 diagnosis. Other popular applications of GANs were segmentation of lungs and superresolution of lung images. The cycleGAN and the conditional GAN were the most commonly used architectures, used in 9 studies each. In addition, 29 (51%) studies used chest X-ray images, while 21 (37%) studies used CT images for the training of GANs. For the majority of the studies (n=47, 82%), the experiments were conducted and results were reported using publicly available data. A secondary evaluation of the results by radiologists/clinicians was reported by only 2 (4%) studies. Conclusions Studies have shown that GANs have great potential to address the data scarcity challenge for lung images in COVID-19. Data synthesized with GANs have been helpful to improve the training of the convolutional neural network (CNN) models trained for the diagnosis of COVID-19. In addition, GANs have also contributed to enhancing the CNNs’ performance through the superresolution of the images and segmentation. This review also identified key limitations of the potential transformation of GAN-based methods in clinical applications.
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Affiliation(s)
- Hazrat Ali
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Zubair Shah
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
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Heidari A, Jafari Navimipour N, Unal M, Toumaj S. Machine learning applications for COVID-19 outbreak management. Neural Comput Appl 2022; 34:15313-15348. [PMID: 35702664 PMCID: PMC9186489 DOI: 10.1007/s00521-022-07424-w] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 05/10/2022] [Indexed: 12/29/2022]
Abstract
Recently, the COVID-19 epidemic has resulted in millions of deaths and has impacted practically every area of human life. Several machine learning (ML) approaches are employed in the medical field in many applications, including detecting and monitoring patients, notably in COVID-19 management. Different medical imaging systems, such as computed tomography (CT) and X-ray, offer ML an excellent platform for combating the pandemic. Because of this need, a significant quantity of study has been carried out; thus, in this work, we employed a systematic literature review (SLR) to cover all aspects of outcomes from related papers. Imaging methods, survival analysis, forecasting, economic and geographical issues, monitoring methods, medication development, and hybrid apps are the seven key uses of applications employed in the COVID-19 pandemic. Conventional neural networks (CNNs), long short-term memory networks (LSTM), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, random forest, and other ML techniques are frequently used in such scenarios. Next, cutting-edge applications related to ML techniques for pandemic medical issues are discussed. Various problems and challenges linked with ML applications for this pandemic were reviewed. It is expected that additional research will be conducted in the upcoming to limit the spread and catastrophe management. According to the data, most papers are evaluated mainly on characteristics such as flexibility and accuracy, while other factors such as safety are overlooked. Also, Keras was the most often used library in the research studied, accounting for 24.4 percent of the time. Furthermore, medical imaging systems are employed for diagnostic reasons in 20.4 percent of applications.
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Affiliation(s)
- Arash Heidari
- Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
- Department of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran
| | | | - Mehmet Unal
- Department of Computer Engineering, Nisantasi University, Istanbul, Turkey
| | - Shiva Toumaj
- Urmia University of Medical Sciences, Urmia, Iran
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8
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Heidari A, Toumaj S, Navimipour NJ, Unal M. A privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchain. Comput Biol Med 2022; 145:105461. [PMID: 35366470 PMCID: PMC8958272 DOI: 10.1016/j.compbiomed.2022.105461] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 03/13/2022] [Accepted: 03/24/2022] [Indexed: 12/16/2022]
Abstract
With the global spread of the COVID-19 epidemic, a reliable method is required for identifying COVID-19 victims. The biggest issue in detecting the virus is a lack of testing kits that are both reliable and affordable. Due to the virus's rapid dissemination, medical professionals have trouble finding positive patients. However, the next real-life issue is sharing data with hospitals around the world while considering the organizations' privacy concerns. The primary worries for training a global Deep Learning (DL) model are creating a collaborative platform and personal confidentiality. Another challenge is exchanging data with health care institutions while protecting the organizations' confidentiality. The primary concerns for training a universal DL model are creating a collaborative platform and preserving privacy. This paper provides a model that receives a small quantity of data from various sources, like organizations or sections of hospitals, and trains a global DL model utilizing blockchain-based Convolutional Neural Networks (CNNs). In addition, we use the Transfer Learning (TL) technique to initialize layers rather than initialize randomly and discover which layers should be removed before selection. Besides, the blockchain system verifies the data, and the DL method trains the model globally while keeping the institution's confidentiality. Furthermore, we gather the actual and novel COVID-19 patients. Finally, we run extensive experiments utilizing Python and its libraries, such as Scikit-Learn and TensorFlow, to assess the proposed method. We evaluated works using five different datasets, including Boukan Dr. Shahid Gholipour hospital, Tabriz Emam Reza hospital, Mahabad Emam Khomeini hospital, Maragheh Dr.Beheshti hospital, and Miandoab Abbasi hospital datasets, and our technique outperform state-of-the-art methods on average in terms of precision (2.7%), recall (3.1%), F1 (2.9%), and accuracy (2.8%).
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Affiliation(s)
- Arash Heidari
- Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran,Department of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran
| | - Shiva Toumaj
- Urmia University of Medical Sciences, Urmia, Iran
| | - Nima Jafari Navimipour
- Department of Computer Engineering, Kadir Has University, Istanbul, Turkey,Corresponding author
| | - Mehmet Unal
- Department of Computer Engineering, Nisantasi University, Istanbul, Turkey
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Suzuki Y, Kido S, Mabu S, Yanagawa M, Tomiyama N, Sato Y. Segmentation of Diffuse Lung Abnormality Patterns on Computed Tomography Images using Partially Supervised Learning. ADVANCED BIOMEDICAL ENGINEERING 2022. [DOI: 10.14326/abe.11.25] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Affiliation(s)
- Yuki Suzuki
- Department of Artificial Intelligence Diagnostic Radiology, Osaka University Graduate School of Medicine
| | - Shoji Kido
- Department of Artificial Intelligence Diagnostic Radiology, Osaka University Graduate School of Medicine
| | - Shingo Mabu
- Graduate School of Sciences and Technology for Innovation, Yamaguchi University
| | - Masahiro Yanagawa
- Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine
| | - Noriyuki Tomiyama
- Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine
| | - Yoshinobu Sato
- Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology
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