Detection and localization of hyperfunctioning parathyroid glands on [¹⁸F]fluorocholine PET/ CT using deep learning – model performance and comparison to human experts

1Fraser WD. Hyperparathyroidism. Lancet 2009; 374: 145-58. doi: 10.1016/ s0140-6736(09)60507-9 Fraser WD Hyperparathyroidism Lancet 2009 374 145 58 10.1016/s0140-6736(09)60507-9Open DOISearch in Google Scholar

2Grimelius L, Akerström G, Johansson H, Bergström R. Anatomy and histopathology of human parathyroid glands. Pathol Annu 1981; 16(Pt 2): 1-24. PMID: 7036057 Grimelius L Akerström G Johansson H Bergström R Anatomy and histopathology of human parathyroid glands Pathol Annu 1981 16Pt 2 1 24 PMID: 7036057Search in Google Scholar

3Cuderman A, Senica K, Rep S, Hocevar M, Kocjan T, Sever, et al. 18F-Fluorocholine PET/CT in primary hyperparathyroidism: superior diagnostic performance to conventional scintigraphic imaging for localization of hyperfunctioning parathyroid glands. J Nucl Med 2019; 61: 577-83. doi: 10.2967/jnumed.119.229914 Cuderman A Senica K Rep S Hocevar M Kocjan T Sever, et al 18F-Fluorocholine PET/CT in primary hyperparathyroidism: superior diagnostic performance to conventional scintigraphic imaging for localization of hyperfunctioning parathyroid glands J Nucl Med 2019 61 577 83 10.2967/jnumed.119.22991431562221Open DOISearch in Google Scholar

4Lezaic L, Rep S, Sever MJ, Kocjan T, Hocevar M, Fettich J. 18F-Fluorocholine PET/CT for localization of hyperfunctioning parathyroid tissue in primary hyperparathyroidism: a pilot study. Eur J Nucl Med Mol Imaging 2014; 41: 2083-9. doi: 10.1007/s00259-014-2837-0 Lezaic L Rep S Sever MJ Kocjan T Hocevar M Fettich J 18F-Fluorocholine PET/CT for localization of hyperfunctioning parathyroid tissue in primary hyperparathyroidism: a pilot study Eur J Nucl Med Mol Imaging 2014 41 2083 9 10.1007/s00259-014-2837-025063039Open DOISearch in Google Scholar

5Graves CE, Hope TA, Kim J, Pampaloni MH, Kluijfhout W, Seib CD, et al. Superior sensitivity of 18F-fluorocholine: PET localization in primary hyperparathyroidism. Surgery 2022; 171: 47-54. doi: 10.1016/j.surg.2021.05.056 Graves CE Hope TA Kim J Pampaloni MH Kluijfhout W Seib CD et al Superior sensitivity of 18F-fluorocholine: PET localization in primary hyperparathyroidism Surgery 2022 171 47 54 10.1016/j.surg.2021.05.05634301418Open DOISearch in Google Scholar

6Michaud L, Balogova S, Burgess A, Ohnona J, Huchet V, Kerrou K, et al. A pilot comparison of 18F-fluorocholine PET/CT, ultrasonography and 123I/99mTc-sestaMIBI dual-phase dual-isotope scintigraphy in the preoperative localization of hyperfunctioning parathyroid glands in primary or secondary hyperparathyroidism. Medicine 2015; 94: e1701. doi: 10.1097/ md.0000000000001701 Michaud L Balogova S Burgess A Ohnona J Huchet V Kerrou K et al A pilot comparison of 18F-fluorocholine PET/CT, ultrasonography and 123I/99mTc-sestaMIBI dual-phase dual-isotope scintigraphy in the preoperative localization of hyperfunctioning parathyroid glands in primary or secondary hyperparathyroidism Medicine 2015 94 e1701 10.1097/md.0000000000001701461678126469908Open DOISearch in Google Scholar

7Kluijfhout WP, Vorselaars WM, van den Berk SA, Vriens MR, Borel Rinkes IH, Valk GD, et al. Fluorine-18 fluorocholine PET-CT localizes hyperparathyroidism in patients with inconclusive conventional imaging. Nucl Med Commun 2016; 37: 1246-52. doi: 10.1097/mnm.0000000000000595 Kluijfhout WP Vorselaars WM van den Berk SA Vriens MR Borel Rinkes IH Valk GD et al Fluorine-18 fluorocholine PET-CT localizes hyperparathyroidism in patients with inconclusive conventional imaging Nucl Med Commun 2016 37 1246 52 10.1097/mnm.000000000000059527612033Open DOISearch in Google Scholar

8Kluijfhout WP, Pasternak JD, Drake FT, Beninato T, Gosnell JE, Shen WT, et al. Use of PET tracers for parathyroid localization: a systematic review and meta-analysis. Langenbecks Arch Surg 2016; 401: 925-35. doi: 10.1007/ s00423-016-1425-0 Kluijfhout WP Pasternak JD Drake FT Beninato T Gosnell JE Shen WT et al Use of PET tracers for parathyroid localization: a systematic review and meta-analysis Langenbecks Arch Surg 2016 401 925 35 10.1007/s00423-016-1425-0508634627086309Open DOISearch in Google Scholar

9Thanseer N, Bhadada SK, Sood A, Mittal BR, Behera A, Gorla A K R, et al. Comparative effectiveness of ultrasonography, 99mTc-sestamibi, and 18F-fluorocholine PET/CT in detecting parathyroid adenomas in patients with primary hyperparathyroidism. Clin Nucl Med 2017; 42: e491-7. doi: 10.1097/rlu.0000000000001845 Thanseer N Bhadada SK Sood A Mittal BR Behera A Gorla A K R et al Comparative effectiveness of ultrasonography, 99mTc-sestamibi, and 18F-fluorocholine PET/CT in detecting parathyroid adenomas in patients with primary hyperparathyroidism Clin Nucl Med 2017 42 e491 7 10.1097/rlu.0000000000001845Open DOISearch in Google Scholar

10Whitman J, Allen IE, Bergsland EK, Suh I, Hope TA. Assessment and comparison of 18F-Fluorocholine PET and 99mTc-sestamibi scans in identifying parathyroid adenomas: a metaanalysis. J Nucl Med 2021; 62: 1285-91. doi: 10.2967/jnumed.120.257303 Whitman J Allen IE Bergsland EK Suh I Hope TA Assessment and comparison of 18F-Fluorocholine PET and 99mTc-sestamibi scans in identifying parathyroid adenomas: a metaanalysis J Nucl Med 2021 62 1285 91 10.2967/jnumed.120.257303888289233452040Open DOISearch in Google Scholar

11Beheshti M, Hehenwarter L, Paymani Z, Rendl G, Imamovic L, Rettenbacher R, et al. 18F-Fluorocholine PET/CT in the assessment of primary hyperparathyroidism compared with 99mTc-MIBI or 99mTc-tetrofosmin SPECT/CT: a prospective dual-centre study in 100 patients. Eur J Nucl Med Mol Imaging 2018; 45: 1762-71. doi: 10.1007/s00259-018-3980-9 Beheshti M Hehenwarter L Paymani Z Rendl G Imamovic L Rettenbacher R et al 18F-Fluorocholine PET/CT in the assessment of primary hyperparathyroidism compared with 99mTc-MIBI or 99mTc-tetrofosmin SPECT/CT: a prospective dual-centre study in 100 patients Eur J Nucl Med Mol Imaging 2018 45 1762 71 10.1007/s00259-018-3980-9609775429516131Open DOISearch in Google Scholar

12Broos WAM, Wondergem M, Knol RJJ, Van der Zant FM. Parathyroid imaging with 18F-fluorocholine PET/CT as a first-line imaging modality in primary hyperparathyroidism: a retrospective cohort study. EJNMMI Res 2019; 9: 72. doi: 10.1186/s13550-019-0544-3 Broos WAM Wondergem M Knol RJJ Van der Zant FM Parathyroid imaging with 18F-fluorocholine PET/CT as a first-line imaging modality in primary hyperparathyroidism: a retrospective cohort study EJNMMI Res 2019 9 72 10.1186/s13550-019-0544-3666922531367807Open DOISearch in Google Scholar

13Hope TA, Graves CE, Calais J, Ehman EC, Johnson GB, Thompson D, et al. Accuracy of 18 F-fluorocholine PET for the detection of parathyroid adenomas: prospective single-center study. J Nucl Med 2021; 62: 1511-6. doi: /10.2967/jnumed.120.256735 Hope TA Graves CE Calais J Ehman EC Johnson GB Thompson D et al Accuracy of 18 F-fluorocholine PET for the detection of parathyroid adenomas: prospective single-center study J Nucl Med 2021 62 1511 6 doi: /10.2967/jnumed.120.25673510.2967/jnumed.120.256735861234333674400Search in Google Scholar

14Rep S, Hocevar M, Vaupotic J, Zdesar U, Zaletel K, Lezaic L. 18F-choline PET/ CT for parathyroid scintigraphy: significantly lower radiation exposure of patients in comparison to conventional nuclear medicine imaging approaches. J Radiol Prot 2018; 38: 343-56. doi: 10.1088/1361-6498/aaa86f Rep S Hocevar M Vaupotic J Zdesar U Zaletel K Lezaic L 18F-choline PET/ CT for parathyroid scintigraphy: significantly lower radiation exposure of patients in comparison to conventional nuclear medicine imaging approaches J Radiol Prot 2018 38 343 56 10.1088/1361-6498/aaa86f29339573Open DOISearch in Google Scholar

15Li Y, Sixou B, Peyrin F. A review of the deep learning methods for medical images super resolution problems. IRBM 2021; 42: 120-33. doi: 10.1016/j. irbm.2020.08.004 Li Y Sixou B Peyrin F A review of the deep learning methods for medical images super resolution problems IRBM 2021 42 120 33 10.1016/j.irbm.2020.08.004Open DOISearch in Google Scholar

16Yang W, Zhang X, Tian Y, Wang W, Xue J-H, Liao Q. Deep learning for single image super-resolution: a brief review. IEEE Trans Multimedia 2019; 21: 3106-21. doi: 10.1109/tmm.2019.2919431 Yang W Zhang X Tian Y Wang W Xue J-H Liao Q Deep learning for single image super-resolution: a brief review IEEE Trans Multimedia 2019 21 3106 21 10.1109/tmm.2019.2919431Open DOISearch in Google Scholar

17Wang L, Chen W, Yang W, Bi F, Yu FR. A state-of-the-art review on image synthesis with generative adversarial networks. IEEE Access 2020; 8: 63514-37. doi: 10.1109/access.2020.2982224 Wang L Chen W Yang W Bi F Yu FR A state-of-the-art review on image synthesis with generative adversarial networks IEEE Access 2020 8 63514-37 10.1109/access.2020.2982224Open DOISearch in Google Scholar

18Liu B, Liu J. Overview of image denoising based on deep learning. J Phys Conf Ser 2019; 1176: 022010. doi: 10.1088/1742-6596/1176/2/022010 Liu B Liu J Overview of image denoising based on deep learning J Phys Conf Ser 2019 1176 022010 10.1088/1742-6596/1176/2/022010Open DOISearch in Google Scholar

19Chartrand G, Cheng PM, Vorontsov E, Drozdzal M, Turcotte S, Pal CJ, et al. Deep learning: a primer for radiologists. RadioGraphics 2017; 37: 2113-31. doi: 10.1148/rg.2017170077 Chartrand G Cheng PM Vorontsov E Drozdzal M Turcotte S Pal CJ et al Deep learning: a primer for radiologists RadioGraphics 2017 37 2113 31 10.1148/rg.201717007729131760Open DOISearch in Google Scholar

20Al-Saffar AAM, Tao H, Talab MA. Review of deep convolution neural network in image classification. In: 2017 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunication. IEEE 2017. p. 26-31. doi: 10.1109/icramet.2017.8253139 Al-Saffar AAM Tao H Talab MA Review of deep convolution neural network in image classification In 2017 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunication. IEEE 2017 p 26 31 10.1109/icramet.2017.8253139Open DOISearch in Google Scholar

21Minaee S, Boykov Y, Porikli F, Plaza A, Kehtarnavaz N, Terzopoulos D. Image segmentation using deep learning: a survey. [Internet]. arXiv: 2001.05566 2020. Available from: https://doi.org/10.48550/arXiv.2001.05566 Minaee S Boykov Y Porikli F Plaza A Kehtarnavaz N Terzopoulos D Image segmentation using deep learning: a survey. [Internet] arXiv 200105566 2020. Available from https://doi.org/10.48550/arXiv.2001.0556610.1109/TPAMI.2021.305996833596172Search in Google Scholar

22Jiao L, Zhang F, Liu F, Yang S, Li L, Feng Z, et al. A survey of deep learning-based object detection. [Internet]. arXiv: 2019. Available from: http://arxiv.org/abs/1907.09408 Jiao L Zhang F Liu F Yang S Li L Feng Z et al A survey of deep learning-based object detection. [Internet] arXiv 2019 Available from http://arxiv.org/abs/1907.09408Search in Google Scholar

23Sahlsten J, Jaskari J, Kivinen J, Turunen L, Jaanio E, Hietala K, et al. Deep learning fundus image analysis for diabetic retinopathy and macular edema grading. Sci Rep 2019; 9: 10750. doi: 10.1038/s41598-019-47181-w Sahlsten J Jaskari J Kivinen J Turunen L Jaanio E Hietala K et al Deep learning fundus image analysis for diabetic retinopathy and macular edema grading Sci Rep 2019 9 10750 10.1038/s41598-019-47181-w665688031341220Open DOISearch in Google Scholar

24Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. S. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542: 115-8. doi: 10.1038/nature21056 Esteva A Kuprel B Novoa RA Ko J Swetter SM Blau HM et al S Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017 542 115 8 10.1038/nature21056838223228117445Open DOISearch in Google Scholar

25Irvin J, Rajpurkar P, Ko M, Yu Y, Ciurea-Ilcus S, Chute C, et al. CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison. Proc Conf AAAI Artif Intell 2019; 33: 590-7. doi: 10.1609/aaai. v33i01.3301590 Irvin J Rajpurkar P Ko M Yu Y Ciurea-Ilcus S Chute C et al CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison Proc Conf AAAI Artif Intell 2019 33 590 7 10.1609/aaai.v33i01.3301590Open DOISearch in Google Scholar

26Nie D, Cao X, Gao Y, Wang L, Shen D. Estimating CT image from MRI data using 3D fully convolutional networks. Deep Learn Data Label Med Appl 2016; 2016: 170-8. doi: 10.1007/978-3-319-46976-8_18 Nie D Cao X Gao Y Wang L Shen D Estimating CT image from MRI data using 3D fully convolutional networks Deep Learn Data Label Med Appl 2016 2016 170 8 10.1007/978-3-319-46976-8_18565458329075680Open DOISearch in Google Scholar

27Torrado-Carvajal A, Vera-Olmos J, Izquierdo-Garcia D, Catalano OA, Morales MA, Margolin J, et al. Dixon-VIBE Deep Learning (DIVIDE) pseudo-CT synthesis for pelvis PET/MR attenuation correction. J Nucl Med 2019; 60: 429-35. doi: 10.2967/jnumed.118.209288 Torrado-Carvajal A Vera-Olmos J Izquierdo-Garcia D Catalano OA Morales MA Margolin J et al Dixon-VIBE Deep Learning (DIVIDE) pseudo-CT synthesis for pelvis PET/MR attenuation correction J Nucl Med 2019 60 429 35 10.2967/jnumed.118.209288691062630166357Open DOISearch in Google Scholar

28Guo R, Hu X, Song H, Xu P, Xu H, Rominger A, et al. Weakly supervised deep learning for determining the prognostic value of 18F-FDG PET/CT in extranodal natural killer/T cell lymphoma, nasal type. Eur J Nucl Med Mol Imaging 2021; 48: 3151-61. doi: 10.1007/s00259-021-05232-3 Guo R Hu X Song H Xu P Xu H Rominger A et al Weakly supervised deep learning for determining the prognostic value of 18F-FDG PET/CT in extranodal natural killer/T cell lymphoma, nasal type Eur J Nucl Med Mol Imaging 2021 48 3151 61 10.1007/s00259-021-05232-3789683333611614Open DOISearch in Google Scholar

29Hwang D, Kang SK, Kim KY, Seo S, Paeng JC, Lee DS, et al. Generation of PET attenuation map for whole-body time-of-flight 18F-FDG PET/MRI using a deep neural network trained with simultaneously reconstructed activity and attenuation maps. J Nucl Med 2019; 60: 1183-9. doi: 10.2967/ jnumed.118.219493 Hwang D Kang SK Kim KY Seo S Paeng JC Lee DS et al Generation of PET attenuation map for whole-body time-of-flight 18F-FDG PET/MRI using a deep neural network trained with simultaneously reconstructed activity and attenuation maps J Nucl Med 2019 60 1183 9 10.2967/jnumed.118.219493668169130683763Open DOISearch in Google Scholar

30Liu F, Jang H, Kijowski R, Bradshaw T, McMillan AB. Deep learning MR imaging-based attenuation correction for PET/MR imaging. Radiology 2018; 286: 676-84. doi: 10.1148/radiol.2017170700 Liu F Jang H Kijowski R Bradshaw T McMillan AB Deep learning MR imaging-based attenuation correction for PET/MR imaging Radiology 2018 286 676 84 10.1148/radiol.2017170700579030328925823Open DOISearch in Google Scholar

31Leynes AP, Yang J, Wiesinger F, Kaushik SS, Shanbhag DD, Seo Y, et al. Zero-Echo-Time and Dixon Deep Pseudo-CT (ZeDD CT): direct generation of pseudo-CT images for pelvic PET/MRI attenuation correction using deep convolutional neural networks with multiparametric MRI. J Nucl Med 2018; 59: 852-8. doi: 10.2967/jnumed.117.198051 Leynes AP Yang J Wiesinger F Kaushik SS Shanbhag DD Seo Y et al Zero-Echo-Time and Dixon Deep Pseudo-CT (ZeDD CT): direct generation of pseudo-CT images for pelvic PET/MRI attenuation correction using deep convolutional neural networks with multiparametric MRI J Nucl Med 2018 59 852 8 10.2967/jnumed.117.198051593253029084824Open DOISearch in Google Scholar

32Blanc-Durand P, Van Der Gucht A, Schaefer N, Itti E, Prior JO. Automatic lesion detection and segmentation of 18F-FET PET in gliomas: a full 3D U-Net convolutional neural network study. PLoS One 2018; 13: e0195798 doi: 10.1371/journal.pone.0195798 Blanc-Durand P Van Der Gucht A Schaefer N Itti E Prior JO Automatic lesion detection and segmentation of 18F-FET PET in gliomas: a full 3D U-Net convolutional neural network study PLoS One 2018 13 e0195798 10.1371/journal.pone.0195798589873729652908Open DOISearch in Google Scholar

33Zhao X, Li L, Lu W, Tan S. Tumor co-segmentation in PET/CT using multi-modality fully convolutional neural network. Phys Med Biol 2018; 64: 015011 doi: 10.1088/1361-6560/aaf44b Zhao X Li L Lu W Tan S Tumor co-segmentation in PET/CT using multi-modality fully convolutional neural network Phys Med Biol 2018 64 015011 10.1088/1361-6560/aaf44b749381230523964Open DOISearch in Google Scholar

34Zhong Z, Kim Y, Plichta K, Allen BG, Zhou L, Buatti J, et al. Simultaneous cosegmentation of tumors in PET-CT images using deep fully convolutional networks. Med Phys 2019; 46(2): 619-33. doi: 10.1002/mp.13331 Zhong Z Kim Y Plichta K Allen BG Zhou L Buatti J et al Simultaneous cosegmentation of tumors in PET-CT images using deep fully convolutional networks Med Phys 2019 462 619 33 10.1002/mp.13331652732730537103Open DOISearch in Google Scholar

35Schwyzer M, Ferraro DA, Muehlematter UJ, Curioni-Fontecedro A, Huellner MW, von Schulthess GK, et al. Automated detection of lung cancer at ultralow dose PET/CT by deep neural networks – initial results. Lung Cancer 2018; 126: 170-3. doi: 10.1016/j.lungcan.2018.11.001 Schwyzer M Ferraro DA Muehlematter UJ Curioni-Fontecedro A Huellner MW von Schulthess GK et al Automated detection of lung cancer at ultralow dose PET/CT by deep neural networks – initial results Lung Cancer 2018 126 170 3 10.1016/j.lungcan.2018.11.00130527183Open DOISearch in Google Scholar

36Hatt M, Laurent B, Ouahabi A, Fayad H, Tan S, Li L, et al. The first MICCAI challenge on PET tumor segmentation. Med Image Anal 2018; 44: 177-95. doi: 10.1016/j.media.2017.12.007 Hatt M Laurent B Ouahabi A Fayad H Tan S Li L et al The first MICCAI challenge on PET tumor segmentation Med Image Anal 2018 44 177 95 10.1016/j.media.2017.12.007746072229268169Open DOISearch in Google Scholar

37Student. The probable error of a mean. Biometrika 1908; 6: 1. doi: 10.2307/2331554 Student The probable error of a mean Biometrika 1908 6 1 10.2307/2331554Open DOISearch in Google Scholar

38Pearson K. X. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Lond Edinb Dublin Philos Mag J Sci 1900; 50: 157-75. doi: 10.1080/14786440009463897 Pearson K. X. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Lond Edinb Dublin Philos Mag J Sci 1900 50 157 75 10.1080/14786440009463897Open DOISearch in Google Scholar

39Jones E, Oliphant T, Peterson P, Others. SciPy.org SciPy Open source Sci. tools Python2. 2001. Jones E Oliphant T Peterson P Others SciPy.org SciPy Open source Sci. tools Python2 2001Search in Google Scholar

40Good IJ. Rational decisions. J R Stat Soc Ser B 1952; 14: 107-14. doi: 10.1111/ j.2517-6161.1952.tb00104.x Good IJ Rational decisions J R Stat Soc Ser B 1952 14 107 14 10.1111/j.2517-6161.1952.tb00104.xOpen DOISearch in Google Scholar

41He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016. doi: 10.1109/cvpr.2016.90 He K Zhang X Ren S Sun J Deep residual learning for image recognition 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 10.1109/cvpr.2016.90Open DOISearch in Google Scholar

42Hara K, Kataoka H, Satoh Y. Learning spatio-temporal features with 3D residual networks for action recognition. 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) 2017. doi: 10.1109/iccvw.2017.373 Hara K Kataoka H Satoh Y Learning spatio-temporal features with 3D residual networks for action recognition 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) 2017 10.1109/iccvw.2017.373Open DOISearch in Google Scholar

43Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017. doi: 10.1109/cvpr.2017.243 Huang G Liu Z Van Der Maaten L Weinberger KQ Densely connected convolutional networks 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 10.1109/cvpr.2017.243Open DOISearch in Google Scholar

44Zagoruyko S, Komodakis N. Wide residual networks. Procedings of the British Machine Vision Conference 2016; 2016. doi: 10.5244/c.30.87 Zagoruyko S Komodakis N Wide residual networks Procedings of the British Machine Vision Conference 2016 2016 10.5244/c.30.87Open DOISearch in Google Scholar

45He K, Zhang X, Ren S, Sun J. Identity mappings in deep residual networks. Computer Vision – ECCV 2016. 2016: 630-45. doi: 10.1007/978-3-31946493-0_38 He K Zhang X Ren S Sun J Identity mappings in deep residual networks Computer Vision – ECCV 2016 2016 630 45 10.1007/978-3-31946493-0_38Open DOISearch in Google Scholar

46Full stack deep learning. Lecture 1: DL fundamentals [Internet]. Fullstackdeeplearning.com. [cited 2022 Aug 28]. Available from: https://fullstackdeeplearning.com/spring2021/lecture-1/ Full stack deep learning Lecture 1: DL fundamentals [Internet]. Fullstackdeeplearning.com [cited 2022 Aug 28] Available from https://fullstackdeeplearning.com/spring2021/lecture-1/Search in Google Scholar

47Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells W, Frangi A. editors. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science 2015; 9351: 234-41. Cham: Springer.doi: 10.1007/978-3-319-24574-4_28 Ronneberger O Fischer P Brox T U-Net: convolutional networks for biomedical image segmentation In: Navab N, Hornegger J, Wells W, Frangi A. editors. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science 2015 9351 234 41 Cham: Springer 10.1007/978-3-319-24574-4_28Open DOISearch in Google Scholar

48Rossum G Van, Drake FL. Python Tutorial, Technical Report CS-R9526. Cent voor Wiskd en Inform 1995. Rossum G Van Drake FL Python Tutorial, Technical Report CS-R9526 Cent voor Wiskd en Inform 1995Search in Google Scholar

49Paszke A, Gross S, Chintala S, Chanan G, Yang E, DeVito Z, et al.. Automatic differentiation in PyTorch. 31st Conf Neural Inf Process Syst 2017. Paszke A Gross S Chintala S Chanan G Yang E DeVito Z et al Automatic differentiation in PyTorch 31st Conf Neural Inf Process Syst 2017Search in Google Scholar

50Stevenson M, Sergeant E, Nunes T, Heuer C, Marshall J, Sanchez J, et al. epiR: Tools for the analysis of epidemiological data. v1.0-15. 2020. [cited 2022 Mar 15]. Available at: https://CRAN.R-project.org/package=epiR Stevenson M Sergeant E Nunes T Heuer C Marshall J Sanchez J et al epiR: Tools for the analysis of epidemiological data. v1.0-15 2020 [cited 2022 Mar 15]. Available at https://CRAN.R-project.org/package=epiRSearch in Google Scholar

51R Development Core Team. R: a language and environment for statistical computing. Vienna; R Foundation for Statistical Computing. Available at: . http://www.R-project.org R Development Core Team R: a language and environment for statistical computing. Vienna; R Foundation for Statistical Computing Available at http://www.R-project.orgSearch in Google Scholar

52McNemar Q. Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 1947; 12: 153-7. doi: 10.1007/bf02295996 McNemar Q. Note on the sampling error of the difference between correlated proportions or percentages Psychometrika 1947 12 153 7 10.1007/bf0229599620254758Open DOISearch in Google Scholar

53Stock C, Hielscher T. DTComPair: comparison of binary diagnostic tests in a paired study design. R package version 1.0.3. [Internet]. 2014. Available from: http://cran.r-project.org/package=DTComPair Stock C Hielscher T DTComPair: comparison of binary diagnostic tests in a paired study design. R package version 1.0.3. [Internet] 2014 Available from http://cran.r-project.org/package=DTComPairSearch in Google Scholar

54Rao SD. Epidemiology of parathyroid disorders. Best Pract Res Clin Endocrinol Metab 2018; 32: 773-80. doi: 10.1016/j.beem.2018.12.003 Rao SD Epidemiology of parathyroid disorders Best Pract Res Clin Endocrinol Metab 2018 32 773 80 10.1016/j.beem.2018.12.00330559041Open DOISearch in Google Scholar

55Somnay YR, Craven M, McCoy KL, Carty SE, Wang TS, Greenberg CC, et al. Improving diagnostic recognition of primary hyperparathyroidism with machine learning. Surgery 2017;161: 1113-21. doi: 10.1016/j.surg.2016.09.044 Somnay YR Craven M McCoy KL Carty SE Wang TS Greenberg CC et al Improving diagnostic recognition of primary hyperparathyroidism with machine learning Surgery 2017161 1113 21 10.1016/j.surg.2016.09.044536795827989606Open DOISearch in Google Scholar

56Press DM, Siperstein AE, Berber E, Shin JJ, Metzger R, Monteiro R, et al. The prevalence of undiagnosed and unrecognized primary hyperparathyroidism: a population-based analysis from the electronic medical record. Surgery 2013; 154: 1232-8. doi: 10.1016/j.surg.2013.06.051 Press DM Siperstein AE Berber E Shin JJ Metzger R Monteiro R et al The prevalence of undiagnosed and unrecognized primary hyperparathyroidism: a population-based analysis from the electronic medical record Surgery 2013 154 1232 8 10.1016/j.surg.2013.06.05124383100Open DOISearch in Google Scholar

57Bilezikian JP, Marcus R, Levine MA, Marcocci C, Silverberg SJ, Potts JT, editors. Parathyroids: basic and clinical concepts. 3rd edition. 2014. Elsevier, Academic Press. Bilezikian JP Marcus R Levine MA Marcocci C Silverberg SJ Potts JT editors Parathyroids: basic and clinical concepts. 3rd edition 2014 Elsevier, Academic PressSearch in Google Scholar

58Marzouki HZ, Chavannes M, Tamilia M, Hier MP, Black MJ, Levental M, et al. Location of parathyroid adenomas: 7-year experience. J Otolaryngol Head Neck Surg 2010; 39: 551-4. PMID: 20828518 Marzouki HZ Chavannes M Tamilia M Hier MP Black MJ Levental M et al Location of parathyroid adenomas: 7-year experience J Otolaryngol Head Neck Surg 2010 39 551 4 PMID: 20828518Search in Google Scholar

59Filser B, Uslar V, Weyhe D, Tabriz N. Predictors of adenoma size and location in primary hyperparathyroidism. Langenbeck’s Arch Surg 2021; 406: 1607. doi: 10.1007/s00423-021-02179-9 Filser B Uslar V Weyhe D Tabriz N Predictors of adenoma size and location in primary hyperparathyroidism Langenbeck’s Arch Surg 2021 406 1607 10.1007/s00423-021-02179-9837094933928428Open DOISearch in Google Scholar

60Shah VN, Bhadada SK, Bhansali A, Behera A, Mittal BR. Changes in clinical & biochemical presentations of primary hyperparathyroidism in India over a period of 20 years. Indian J Med Res 2014; 139: 694-9. PMID: 25027078 Shah VN Bhadada SK Bhansali A Behera A Mittal BR Changes in clinical & biochemical presentations of primary hyperparathyroidism in India over a period of 20 years Indian J Med Res 2014 139 694 9 PMID: 25027078Search in Google Scholar

61Xie S, Girshick R, Dollár P, Tu Z, He K. Aggregated residual transformations for deep neural networks. Proc - 30th IEEE Conf Comput Vis Pattern Recognition, CVPR 2017 2017. doi: 10.1109/cvpr.2017.634 Xie S Girshick R Dollár P Tu Z He K Aggregated residual transformations for deep neural networks Proc - 30th IEEE Conf Comput Vis Pattern Recognition, CVPR 2017 2017 10.1109/cvpr.2017.634Open DOISearch in Google Scholar

62Gao S, Cheng MM, Zhao K, Zhang XY, Yang MH, Torr PHS. Res2Net: a new multi-scale backbone architecture. IEEE Trans Pattern Anal Mach Intell 2019. doi: 10.1109/TPAMI.2019.2938758 Gao S Cheng MM Zhao K Zhang XY Yang MH Torr PHS Res2Net: a new multi-scale backbone architecture IEEE Trans Pattern Anal Mach Intell 2019 10.1109/TPAMI.2019.293875831484108Open DOISearch in Google Scholar

63Chen S, Tan X, Wang B, Hu X. Reverse attention for salient object detection. Computer Vision – ECCV 2018 2018; 236-52. doi: 10.1007/978-3-03001240-3_15 Chen S Tan X Wang B Hu X Reverse attention for salient object detection Computer Vision – ECCV 2018 2018 236 52 10.1007/978-3-03001240-3_15Open DOISearch in Google Scholar

64Bailly A, Blanc C, Francis É, Guillotin T, Jamal F, Wakim B, et al. Effects of dataset size and interactions on the prediction performance of logistic regression and deep learning models. Comput Methods Programs Biomed 2022; 213: 106504 doi: 10.1016/j.cmpb.2021.106504 Bailly A Blanc C Francis É Guillotin T Jamal F Wakim B et al Effects of dataset size and interactions on the prediction performance of logistic regression and deep learning models Comput Methods Programs Biomed 2022 213 106504 10.1016/j.cmpb.2021.10650434798408Open DOISearch in Google Scholar

65Hochreiter S, Schmidhuber J. Long Short-Term Memory. Neural Comput 1997; 9: 1735-80. doi: 10.1162/neco.1997.9.8.1735 Hochreiter S Schmidhuber J Long Short-Term Memory Neural Comput 1997 9 1735 80 10.1162/neco.1997.9.8.17359377276Open DOISearch in Google Scholar

66Togo R, Hirata K, Manabe O, Ohira H, Tsujino I, Magota K, et al. Cardiac sarcoidosis classification with deep convolutional neural network-based features using polar maps. Comput Biol Med 2019; 104: 81-6. doi: 10.1016/j. compbiomed.2018.11.008 Togo R Hirata K Manabe O Ohira H Tsujino I Magota K et al Cardiac sarcoidosis classification with deep convolutional neural network-based features using polar maps Comput Biol Med 2019 104 81 6 10.1016/j.compbiomed.2018.11.00830447397Open DOISearch in Google Scholar

67Lu D, Popuri K, Ding GW, Balachandar R, Beg MF. Multiscale deep neural network based analysis of FDG-PET images for the early diagnosis of Alzheimer’s disease. Med Image Anal 2018; 46: 26-34. doi: 10.1016/j.media.2018.02.002 Lu D Popuri K Ding GW Balachandar R Beg MF Multiscale deep neural network based analysis of FDG-PET images for the early diagnosis of Alzheimer’s disease Med Image Anal 2018 46 26 34 10.1016/j.media.2018.02.00229502031Open DOISearch in Google Scholar

68Ma L, Ma C, Liu Y, Wang X. Thyroid diagnosis from SPECT images using convolutional neural network with optimization. Comput Intell Neurosci 2019; 2019: 6212759. doi: 10.1155/2019/6212759 Ma L Ma C Liu Y Wang X Thyroid diagnosis from SPECT images using convolutional neural network with optimization Comput Intell Neurosci 2019 2019 6212759 10.1155/2019/6212759635054730766599Open DOISearch in Google Scholar

69Niu Z, Zhong G, Yu H. A review on the attention mechanism of deep learning. Neurocomputing 2021; 452: 48-62. doi: 10.1016/j.neucom.2021.03.091 Niu Z Zhong G Yu H A review on the attention mechanism of deep learning Neurocomputing 2021 452 48 62 10.1016/j.neucom.2021.03.091Open DOISearch in Google Scholar

70Liu Y, Zhang Y, Wang Y, Hou F, Yuan J, Tian J, et al. A survey of visual transformers. arXiv [csCV] [Internet]. 2021 [cited 2022 Aug 28]; Available from: http://arxiv.org/abs/2111.06091 Liu Y Zhang Y Wang Y Hou F Yuan J Tian J et al A survey of visual transformers arXiv [csCV] [Internet] 2021 [cited 2022 Aug 28]; Available from http://arxiv.org/abs/2111.06091Search in Google Scholar

71Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A. Learning deep features for discriminative localization. arXiv [csCV] [Internet]. 2015 [cited 2022 Aug 28]; Available from: http://arxiv.org/abs/1512.04150 Zhou B Khosla A Lapedriza A Oliva A Torralba A Learning deep features for discriminative localization arXiv [csCV] [Internet] 2015 [cited 2022 Aug 28]; Available from http://arxiv.org/abs/1512.0415010.1109/CVPR.2016.319Search in Google Scholar

72Ancona M, Ceolini E, Öztireli C, Gross M. Towards better understanding of gradient-based attribution methods for deep neural networks. arXiv [csLG] [Internet]. 2017 [cited 2022 Aug 28]; Available from: http://arxiv.org/abs/1711.06104 Ancona M Ceolini E Öztireli C Gross M Towards better understanding of gradient-based attribution methods for deep neural networks. arXiv [csLG] [Internet] 2017 [cited 2022 Aug 28]; Available from http://arxiv.org/abs/1711.06104Search in Google Scholar