KIEE - The Transactions of the Korean Institute of Electrical Engineers

1

M.S. Tonetti, Impact of the global burden of periodontal diseases on health, nutrition and wellbeing of mankind: A call for global action, Journal of Clinical Periodontology, Vol. 44, No. 5, pp. 456-462

2

G.C. Armitage, 1999, Development of a classification system for periodontal diseases and conditions. Annals Periodontology, Vol. 4, No. 1, pp. 1-6

3

J.G. Caton, 2018, A new classification scheme for periodontal and peri-implant diseases and conditions - Introduction and key changes from the 1999 classification, Journal of Periodontology, Vol. 89, pp. s1-S8

4

M.S. Tonetti, 2018, Kornman Staging and grading of periodontitis: Framework and proposal of a new classification and case definition, Journal of Clinical Periodontology, Vol. 45, No. 20, pp. s149-S161

5

H.P. Chan, 2020, Computer-aided diagnosis in the era of deep learning, Medical Physics, Vol. 47, No. 5, pp. E218-E227

6

O. Kwon, 2020, Automatic diagnosis for cysts and tumors of both jaws on panoramic radiographs using a deep convolution neural network, Dentomaxillofacial Radiology, 20200185, Vol. 49, No. 8

7

R. Yamashita, 2018, Convolutional neural networks: an overview and application in radiology, Insights Imaging, Vol. 9, No. 4, pp. 611-629

8

M. Kallenberg, 2016, Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring, IEEE Transactions on Medical Imaging, Vol. 35, No. 5, pp. 1322-1331

9

A. Esteva, 2017, Dermatologist-level classification of skin cancer with deep neural networks, Nature, Vol. 542, No. 7639, pp. 115-118

10

A.Y. Hannun, 2019, Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network, Nature Medicine, Vol. 25, No. 3, pp. 65-69

11

H. Lee, M. Park, J. Kim, 2017, Cephalometric landmark detection in dental x-ray images using convolutional neural networks, Proceedings of the Medical Imaging 2017: Computer-aided Diagnosis, 10134: 101341W

12

O.P. Ronneberger, 2015, Dental X-ray image segmentation using a U-shaped Deep Convolutional network, Proceedings of the International Symposium on Biomedical Imaging

13

Y. Miki, 2017, Classification of teeth in cone-beam CT using deep convolutional neural network, Computers in Biology and Medicine, Vol. 80, pp. 24-29

14

T. Hiraiwa, 2019, A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography, Dentomaxillofacial Radiology, 20180218, Vol. 48, No. 3

15

M. Murata, 2018, Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography, Oral Radiology, Vol. 35, pp. 301-307

16

J. Krois, 2019, Deep Learning for the Radiographic Detection of periodontal Bone Loss, Scientific Reports, Vol. 9, No. 1, pp. 8495

17

J. Kim, 2019, DeNTNet: Deep Neural Transfer Network for the detection of periodontal bone loss using panoramic dental radiographs, Scientific Reports, 17615, Vol. 9, No. 1

18

C. Chen, 2020, Improving the Generalizability of Convolutional Neural Network-Based Segmentation on CMR Images, Frontiers in Cardiovascular Medicine, Vol. 7, pp. 105

19

H.J. Chang, 2020, Deep Learning Hybrid Method to Automatically Diagnose Periodontal Bone Loss and Stage Periodontitis, Scientific Reports, Vol. 10, No. 1, pp. 7531

20

K.M. He, 2020, Mask R-CNN, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 42, No. 2, pp. 386-397

21

J. Redmon, 2016, You only look once: Unified, real-time object detection, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

22

A. Bochkovskiy, 2020, YOLOv4: Optimal Speed and Accuracy of Object Detection, arXiv preprint arXiv:.10934

23

T.-Y. Lin, 2017, Feature pyramid networks for object detection, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

24

W.J. Yi, 2007, Comparison of trabecular bone anisotropies based on fractal dimensions and mean intercept length determined by principal axes of inertia, Medical & Biological Engineering & Computing, Vol. 45, No. 4, pp. 357-364

25

W.J. Yi, Oral Medicine, Direct measurement of trabecular bone anisotropy using directional fractal dimension and principal axes of inertia, Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology, Vol. 104, No. 1, pp. 110-116

26

M.S. Tonetti, 2018, Staging and grading of periodontitis: Framework and proposal of a new classification and case definition, Journal of Periodontology, Vol. 89, pp. s159-S172

27

M. Polak, 2009, An evaluation metric for image segmentation of multiple objects, Image and Vision Computing, Vol. 27, No. 8, pp. 1223-1227

28

M. Seyedhosseini, 2016, Semantic Image Segmentation with Contextual Hierarchical Models, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 38, No. 5, pp. 951-964

29

C. Szegedy, 2013, Deep neural networks for object detection, Proceedings of the Advances in Neural Information Processing Systems

30

R. Girshick, 2014, Rich feature hierarchies for accurate object detection and semantic segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

31

R.E. Persson, 2003, Comparison between panoramic and intraoral radiographs for the assessment of alveolar bone levels in a periodontal maintenance population, Journal of Clinical Periodontology, Vol. 30, No. 9, pp. 833-839

KIEEThe Transactions of
the Korean Institute of Electrical Engineers

The Transactions of the Korean Institute of Electrical Engineers

ISO Journal TitleTrans. Korean. Inst. Elect. Eng.

Journal Search

Journal XML

References

KIEEThe Transactions ofthe Korean Institute of Electrical Engineers