This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Abaza, R. 2020a. Avoiding Positive Margins During Robotic Partial Nephrectomy presented by Ronney Abaza, Seattle Science Foundation, Seattle, Washington, Available at: https://www.youtube.com/watch?v=C3VTbb_1GAM&ab_channel=SeattleScienceFoundation (Accessed March 20, 2021).AbazaR.2020aSeattle Science FoundationSeattle, WashingtonAvailable at: https://www.youtube.com/watch?v=C3VTbb_1GAM&ab_channel=SeattleScienceFoundation(AccessedMarch 20, 2021)Search in Google Scholar
Abaza, R. 2020b. Robotic Partial Nephrectomy for Complex Tumors presented by Ronney Abaza, Seattle Science Foundation, Seattle, Washington, Available at: https://www.youtube.com/watch?v=vvf16vBrgxQ&t=662s&ab_channel=SeattleScienceFoundation (Accessed March 20, 2021).AbazaR.2020bSeattle Science FoundationSeattle, WashingtonAvailable at: https://www.youtube.com/watch?v=vvf16vBrgxQ&t=662s&ab_channel=SeattleScienceFoundation(AccessedMarch 20, 2021)Search in Google Scholar
American Institute of Minimally Invasive Surgery 2019. DA VINCI XI, American Medical Center, Available at: https://www.aimisrobotics.com/da-vinci-xi/ (Accessed June 26, 2021).American Institute of Minimally Invasive Surgery2019American Medical CenterAvailable at: https://www.aimisrobotics.com/da-vinci-xi/(AccessedJune 26, 2021)Search in Google Scholar
Alexey, A. B. n.d. darknet, Available at: https://github.com/AlexeyAB/darknet.Alexey, A. Bn.d.Available at:https://github.com/AlexeyAB/darknetSearch in Google Scholar
Aly, G. H., Marey, M., El-Sayed, S. A. and Tolba, M. F. 2021. YOLO based breast masses detection and classification in full-field digital mammograms. Computer Methods and Programs in Biomedicine 200: 105823. Available at: https://doi.org/10.1016/j.cmpb.2020.105823.AlyG. H.MareyM.El-SayedS. A.TolbaM. F.2021YOLO based breast masses detection and classification in full-field digital mammograms200105823Available at: https://doi.org/10.1016/j.cmpb.2020.10582310.1016/j.cmpb.2020.105823Search in Google Scholar
Asadnia, M., Kottapalli, A. G. P., Miao, J., Benson, R. A., Sabbagh, A., Kropelnicki, P. and Tsai, J. 2013. High temperature characterization of PZT (0.52/0.48) thin-film pressure sensors. Journal of Micromechanics and Microengineering 24(1): 015017.AsadniaM.KottapalliA. G. P.MiaoJ.BensonR. A.SabbaghA.KropelnickiP.TsaiJ.2013High temperature characterization of PZT (0.52/0.48) thin-film pressure sensors24(1):01501710.1088/0960-1317/24/1/015017Search in Google Scholar
Asadnia, M., Chua, L. H., Qin, X. and Talei, A. 2014. Improved particle swarm optimization–based artificial neural network for rainfall-runoff modeling. Journal of Hydrologic Engineering 19(7): 1320–1329.AsadniaM.ChuaL. H.QinX.TaleiA.2014Improved particle swarm optimization–based artificial neural network for rainfall-runoff modeling19(7):1320132910.1061/(ASCE)HE.1943-5584.0000927Search in Google Scholar
Asadnia, M., Yazdi, M. S. and Khorasani, A. 2010. An improved particle swarm optimization based on neural network for surface roughness optimization in face milling of 6061-T6 Aluminum. International Journal of Applied Engineering Research 5(19): 3191–3201.AsadniaM.YazdiM. S.KhorasaniA.2010An improved particle swarm optimization based on neural network for surface roughness optimization in face milling of 6061-T6 Aluminum5(19):31913201Search in Google Scholar
Asadnia, M., Khorasani, A. M. and Warkiani, M. E. 2017. An accurate PSO-GA based neural network to model growth of carbon nanotubes. Journal of Nanomaterials 2017.AsadniaM.KhorasaniA. M.WarkianiM. E.2017An accurate PSO-GA based neural network to model growth of carbon nanotubes201710.1155/2017/9702384Search in Google Scholar
Aubreville, M., Bertram, C. A., Donovan, T. A., Marzahl, C., Maier, A. and Klopfleisch, R. 2020. A completely annotated whole slide image dataset of canine breast cancer to aid human breast cancer research. Scientific Data 7(1): 417, doi: 10.1038/s41597-020-00756-z.AubrevilleM.BertramC. A.DonovanT. A.MarzahlC.MaierA.KlopfleischR.2020A completely annotated whole slide image dataset of canine breast cancer to aid human breast cancer research7(1):417doi:10.1038/s41597-020-00756-z769962733247116Open DOISearch in Google Scholar
Bazaz, S. R., Mehrizi, A. A., Ghorbani, S., Vasilescu, S., Asadnia, M. and Warkiani, M. E. 2018. A hybrid micromixer with planar mixing units. RSC Advances 8(58): 33103–33120.BazazS. R.MehriziA. A.GhorbaniS.VasilescuS.AsadniaM.WarkianiM. E.2018A hybrid micromixer with planar mixing units8(58):331033312010.1039/C8RA05763JSearch in Google Scholar
Bennet, M., Thamilvalluvan, B., Alphonse, P. P., Thendralarasi, D. R., Sujithra, K. J. I. J. O. S. S. and Systems, I. 2017. Performance and analysis of automatic license plate localization and recognition from video sequences. International Journal on Smart Sensing and Intelligent Systems 10: 330–343.BennetM.ThamilvalluvanB.AlphonseP. P.ThendralarasiD. R.SujithraK. J. I. J. O. S. S.SystemsI.2017Performance and analysis of automatic license plate localization and recognition from video sequences1033034310.21307/ijssis-2017-255Search in Google Scholar
Bochkovskiy, A., Wang, C. and Liao, H. 2020. YOLOv4: optimal speed and accuracy of object detection. Computer Vision and Pattern Recognition 1.BochkovskiyA.WangC.LiaoH.2020YOLOv4: optimal speed and accuracy of object detection1Search in Google Scholar
Brunese, L., Mercaldo, F., Reginelli, A., Santone, A. J. C. M. and Biomedicine, P. I. 2020. Explainable deep learning for pulmonary disease and Coronavirus COVID-19 detection from x-rays vol. 196: 105608–105608.BruneseL.MercaldoF.ReginelliA.SantoneA. J. C. M.BiomedicineP. I.2020vol.19610560810560810.1016/j.cmpb.2020.105608783186832599338Search in Google Scholar
Charibaldi, N., Harjoko, A., Azhari, Hisyam, B. J. I. J. O. S. S. and Systems, I. 2018. A new HGA-FLVQ model for Mycobacterium Tuberculosis detection,”. International Journal on Smart Sensing and Intelligent Systems 11: 1–13.CharibaldiN.HarjokoA.AzhariHisyamB. J. I. J. O. S. S.SystemsI.2018A new HGA-FLVQ model for Mycobacterium Tuberculosis detection,”1111310.21307/ijssis-2018-028Search in Google Scholar
Chen, Z., Zhang, T. and Ouyang, C. 2018. End-to-end airplane detection using transfer learning in remote sensing images. Remote Sensing 10(1): 139, doi: 10.3390/rs10010139.ChenZ.ZhangT.OuyangC.2018End-to-end airplane detection using transfer learning in remote sensing images10(1):139doi:10.3390/rs10010139Open DOISearch in Google Scholar
Chollet, F. 2017. Deep Learning with Python. Manning Publications, Shelter Island, NY.CholletF.2017Manning PublicationsShelter Island, NYSearch in Google Scholar
Chung, A. G., Khalvati, F., Shafiee, M. J., Haider, M. A. and Wong, A. 2015. Prostate cancer detection via a quantitative radiomics-driven conditional random field framework. IEEE Access 3: 2531–2541, doi: 10.1109/ACCESS.2015.2502220.ChungA. G.KhalvatiF.ShafieeM. J.HaiderM. A.WongA.2015Prostate cancer detection via a quantitative radiomics-driven conditional random field framework325312541doi:10.1109/ACCESS.2015.2502220Open DOISearch in Google Scholar
David, B. and Samadi, M. D. n.d. History and The Future of Robotic Surgery, Robotic Oncology, Available at: https://www.roboticoncology.com/history-of-robotic-surgery/.DavidB.SamadiM. Dn.d.Available at: https://www.roboticoncology.com/history-of-robotic-surgery/Search in Google Scholar
Depeursinge, A., Vargas, A., Platon, A., Geissbuhler, A., Poletti, P. -A. and Müller, H. 2012. Building a reference multimedia database for interstitial lung diseases. Computerized Medical Imaging and Graphics 36(3): 227–238, Available at: https://doi.org/10.1016/j.compmedimag.2011.07.003.DepeursingeA.VargasA.PlatonA.GeissbuhlerA.PolettiP. -A.MüllerH.2012Building a reference multimedia database for interstitial lung diseases36(3):227238Available at: https://doi.org/10.1016/j.compmedimag.2011.07.00310.1016/j.compmedimag.2011.07.00321803548Search in Google Scholar
Engel, D., Jason, D. and Engel, M. D. 2016. Robotic Partial Nephrectomy, Oroogic Surgeons of Washington, Available at: https://www.youtube.com/watch?v=UXWjNqTwb_4&ab_channel=JasonD.Engel%2CM.D (Accessed March 20, 2021).EngelD.JasonD.EngelM. D.2016Available at: https://www.youtube.com/watch?v=UXWjNqTwb_4&ab_channel=JasonD.Engel%2CM.D(AccessedMarch 20, 2021)Search in Google Scholar
Ge, L., Dan, D. and Hui, L. 2020. An accurate and robust monitoring method of full-bridge traffic load distribution based on YOLO-v3 machine vision. Structural Control and Heath Monitoring 27.GeL.DanD.HuiL.2020An accurate and robust monitoring method of full-bridge traffic load distribution based on YOLO-v3 machine vision2710.1002/stc.2636Search in Google Scholar
Geron, A. 2019. Hands-on Machine Learning with Scikit-learn, Keras & TensorFlow. o’Reiley Media, Inc, Sebatopol, CA.GeronA.2019o’Reiley Media, IncSebatopol, CASearch in Google Scholar
GlobalCastMD. n.d. 02 Robotic partial nephrectomycourse tips for retroperitoneral partial nephrectomy James Porter HD, Available at: https://www.youtube.com/watch?v=S80t7cnFLus&ab_channel=GlobalCastMD.GlobalCastMDn.d.Available at: https://www.youtube.com/watch?v=S80t7cnFLus&ab_channel=GlobalCastMDSearch in Google Scholar
Hadjiyski, N. 2020. Kidney cancer staging: deep learning neural network based approach. 2020 International Conference on e-Health and Bioengineering (EHB), October 29–30, pp. 1–4, doi: 10.1109/EHB50910.2020.9280188.HadjiyskiN.2020Kidney cancer staging: deep learning neural network based approachOctober29–30pp.14doi:10.1109/EHB50910.2020.9280188Open DOISearch in Google Scholar
Hagihghi, R., Razmjou, A., Orooji, Y., Warkiani, M. E. and Asadnia, M. 2020. A miniaturized piezoresistive flow sensor for real-time monitoring of intravenous infusion. Journal of Biomedical Materials Research Part B: Applied Biomaterials 108(2): 568–576.HagihghiR.RazmjouA.OroojiY.WarkianiM. E.AsadniaM.2020A miniaturized piezoresistive flow sensor for real-time monitoring of intravenous infusion108(2):56857610.1002/jbm.b.3441231106527Search in Google Scholar
Hammal, S., Bourahla, N. and Laouami, N. 2020. Neural-network based prediction of inelastic response spectra. Civil Engineering Journal 6(6): 1124–1135.HammalS.BourahlaN.LaouamiN.2020Neural-network based prediction of inelastic response spectra6(6):1124113510.28991/cej-2020-03091534Search in Google Scholar
Hampton, L. 2015. da Vinci Xi Right Robotic Partial Nephrectomy-Unedited, VCUrobotics, Richmond, VI, Available at: https://www.youtube.com/watch?v=6eyZzoScc54&ab_channel=VCUrobotics (Accessed March 20, 2021).HamptonL.2015VCUroboticsRichmond, VIAvailable at: https://www.youtube.com/watch?v=6eyZzoScc54&ab_channel=VCUrobotics(AccessedMarch 20, 2021)Search in Google Scholar
He, K., Zhang, X., Ren, S. and Sun, J. 2016. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27–30, pp. 770–778, doi: 10.1109/CVPR.2016.90.HeK.ZhangX.RenS.SunJ.2016Deep residual learning for image recognitionJune27–30pp.770778doi:10.1109/CVPR.2016.90Open DOISearch in Google Scholar
Inc, A. and Grove, B. 2021. Indocyanine Green Side Effects, Available at: https://www.drugs.com/sfx/indocyanine-green-side-effects.html.IncA.GroveB.2021Available at: https://www.drugs.com/sfx/indocyanine-green-side-effects.htmlSearch in Google Scholar
Junnumtuam, S., Niwitpong, S. -A. and Niwitpong, S. 2021. The Bayesian confidence interval for coefficient of variation of zero-inflated poisson distribution with application to daily COVID-19 deaths in Thailand. Emerging Science Journal 5: 62–76.JunnumtuamS.NiwitpongS. -A.NiwitpongS.2021The Bayesian confidence interval for coefficient of variation of zero-inflated poisson distribution with application to daily COVID-19 deaths in Thailand5627610.28991/esj-2021-SPER-05Search in Google Scholar
Kharate, G., Ghotkar, A. J. I. J. O. S. S. and Systems, I. 2016. Vision based multi-feature hand gesture recognition for Indian sign language manual signs. International Journal on Smart Sensing and Intelligent Systems 9: 124–147.KharateG.GhotkarA. J. I. J. O. S. S.SystemsI.2016Vision based multi-feature hand gesture recognition for Indian sign language manual signs912414710.21307/ijssis-2017-863Search in Google Scholar
Khorasani, A. M., Gibson, I., Asadnia, M. and O’Neill, W. 2018. Mass transfer and flow in additive manufacturing of a spherical component. International Journal of Advanced Manufacturing Technology 96: 3711–3718.KhorasaniA. M.GibsonI.AsadniaM.O’NeillW.2018Mass transfer and flow in additive manufacturing of a spherical component963711371810.1007/s00170-017-1483-7Search in Google Scholar
Kibel, A. 2018. Robotic Assisted Laparoscopic Partial Nephrectomy, Brigham and Women’s Hospital, Boston, MA, Available at: https://www.youtube.com/watch?v=GQm90mWVMJM&ab_channel=BrighamAndWomen%27sHospital (Accessed March 20, 2021).KibelA.2018Brigham and Women’s HospitalBoston, MAAvailable at: https://www.youtube.com/watch?v=GQm90mWVMJM&ab_channel=BrighamAndWomen%27sHospital(AccessedMarch 20, 2021)Search in Google Scholar
Kottapalli, A. G. P., Asadnia, M., Miao, J. and Triantafyllou, M. 2015. Soft polymer membrane micro-sensor arrays inspired by the mechanosensory lateral line on the blind cavefish. Journal of Intelligent Material Systems and Structures 26(1): 38–46.KottapalliA. G. P.AsadniaM.MiaoJ.TriantafyllouM.2015Soft polymer membrane micro-sensor arrays inspired by the mechanosensory lateral line on the blind cavefish26(1):384610.1177/1045389X14521702Search in Google Scholar
Li, M., Zhang, Z., Lei, L., Wang, X. and Guo, X. 2020. Agricultural greenhouses detection in high-resolution satellite images based on convolutional neural networks: comparison of faster R-CNN, YOLO v3 and SSD. Sensors 20(17), 10.3390/s20174938.LiM.ZhangZ.LeiL.WangX.GuoX.2020Agricultural greenhouses detection in high-resolution satellite images based on convolutional neural networks: comparison of faster R-CNN, YOLO v3 and SSD20(17)10.3390/s20174938Open DOISearch in Google Scholar
Lin, T., Goyal, P., Girshick, R., He, K. and Dollár, P. 2020. Focal loss for dense object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 42(2): 318–327, doi: 10.1109/TPAMI.2018.2858826.LinT.GoyalP.GirshickR.HeK.DollárP.2020Focal loss for dense object detection42(2):318327doi:10.1109/TPAMI.2018.2858826Open DOISearch in Google Scholar
Long, J., Shelhamer, E. and Darrell, T. 2015. Fully convolutional networks for semantic segmentation. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7–12, pp. 3431–3440, doi: 10.1109/CVPR.2015.7298965.LongJ.ShelhamerE.DarrellT.2015Fully convolutional networks for semantic segmentationJune7–12pp.34313440doi:10.1109/CVPR.2015.7298965Open DOISearch in Google Scholar
Mahmud, M. A. P., Azadi, F. E. S., Myers, M., Pejcic, B., Abbassi, R., Razmjou, A. and Asadnia, A. 2020. Recent progress in sensing nitrate, nitrite, phosphate, and ammonium in aquatic environment. Chemosphere 259: 127492.MahmudM. A. P.AzadiF. E. S.MyersM.PejcicB.AbbassiR.RazmjouA.AsadniaA.2020Recent progress in sensing nitrate, nitrite, phosphate, and ammonium in aquatic environment25912749210.1016/j.chemosphere.2020.127492Search in Google Scholar
Moshizi, S. A., Azadi, S., Belford, A., Ramjou, A., Qu, S., Han, Z. J. and Asadnia, M. 2020. Development of an ultra-sensitive and flexible piezoresistive flow sensor using vertical graphene nanosheets. Nano-micro Letters 12.MoshiziS. A.AzadiS.BelfordA.RamjouA.QuS.HanZ. J.AsadniaM.2020Development of an ultra-sensitive and flexible piezoresistive flow sensor using vertical graphene nanosheets1210.1007/s40820-020-00446-wSearch in Google Scholar
Nakhaeinia, D., Payeur, P., Aragon, A. C., Cretu, A-. M., Laganiere, R. and Macknojia, R. 2016. Surface following with an rgb-d vision-guided robotic system for automated and rapid vehicle inspection. International Journal on Smart Sensing and Intelligent Systems 9: 419–447.NakhaeiniaD.PayeurP.AragonA. C.CretuA-. M.LaganiereR.MacknojiaR.2016Surface following with an rgb-d vision-guided robotic system for automated and rapid vehicle inspection941944710.21307/ijssis-2017-877Search in Google Scholar
National Kidney Foundation. n.d. Nephrectomy, Available at: https://www.kidney.org/atoz/content/nephrectomy.National Kidney Foundationn.d.Available at:https://www.kidney.org/atoz/content/nephrectomySearch in Google Scholar
N. Cancer. n.d. Kidney cancer: stages Available at: https://www.cancer.net/cancer-types/kidney-cancer/stages.N. Cancern.d.Available at: https://www.cancer.net/cancer-types/kidney-cancer/stagesSearch in Google Scholar
Ohira, N. 2018. Memory-efficient 3D connected component labeling with parallel computing. Signal, Image and Video Processing 12(3): 429–436, doi: 10.1007/s11760-017-1175-7.OhiraN.2018Memory-efficient 3D connected component labeling with parallel computing12(3):429436doi:10.1007/s11760-017-1175-7Open DOISearch in Google Scholar
Pantanowitz, L., Garza, G., Bien, L., Heled, R., Laifenfeld, D., Linhart, C., Sandbank, J., Shach, A. and Shalev, V. 2020. An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study. The Lancet Digital Health 2(8): e407–e416, doi: 10.1016/S2589-7500(20)30159-X.PantanowitzL.GarzaG.BienL.HeledR.LaifenfeldD.LinhartC.SandbankJ.ShachA.ShalevV.2020An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study2(8):e407e416doi:10.1016/S2589-7500(20)30159-XOpen DOISearch in Google Scholar
Poggiali, E., Dacrema, A. and Bastoni, D. 2020. Can Lung US Help Critical Care Clinicians in the early diagnosis of Novel Coronavirus (COVID-19) pneumonia? Radiology 295.PoggialiE.DacremaA.BastoniD.2020Can Lung US Help Critical Care Clinicians in the early diagnosis of Novel Coronavirus (COVID-19) pneumonia?29510.1148/radiol.2020200847723338132167853Search in Google Scholar
Porter, J. 2015. LIVE SURGERY: Retroperitoneal Robotic Partial Nephrectomy, Seattle Science Foundation, Seattle, Washington, Available at: https://www.youtube.com/watch?v=nwrbKNbLCv8&t=5045s&ab_channel=SeattleScienceFoundation (Accessed March 2021).PorterJ.2015Seattle Science FoundationSeattle, WashingtonAvailable at: https://www.youtube.com/watch?v=nwrbKNbLCv8&t=5045s&ab_channel=SeattleScienceFoundation(AccessedMarch 2021)Search in Google Scholar
P. N. U. Specialist. Robotic partial nephrectomy comparisons https://www.youtube.com/watch?v=epvKkH3ekRo&ab_channel=PacificNorthwestUrologySpecialists%2CPLLC, Available at: https://www.youtube.com/watch?v=epvKkH3ekRo&ab_channel=PacificNorthwestUrologySpecialists%2CPLLC.P. N. U. Specialisthttps://www.youtube.com/watch?v=epvKkH3ekRo&ab_channel=PacificNorthwestUrologySpecialists%2CPLLC, Available at: https://www.youtube.com/watch?v=epvKkH3ekRo&ab_channel=PacificNorthwestUrologySpecialists%2CPLLCSearch in Google Scholar
Razfar, M., Asadnia, M., Haghshenas, M. and Farahnakian, M. 2010. Optimum surface roughness prediction in face milling X20Cr13 using particle swarm optimization algorithm. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 224(11): 1645–1653.RazfarM.AsadniaM.HaghshenasM.FarahnakianM.2010Optimum surface roughness prediction in face milling X20Cr13 using particle swarm optimization algorithm224(11):1645165310.1243/09544054JEM1809Search in Google Scholar
Razmjou, A., Asadnia, M., Ghaebi, O., Yang, H-. C., Warkiani, M. E., Hou, J. and Chen, V. 2017. Preparation of iridescent 2D photonic crystals by using a mussel-inspired spatial patterning of ZIF-8 with potential applications in optical switch and chemical sensor. ACS Applied Materials & Interfaces 9(43): 38076–38080.RazmjouA.AsadniaM.GhaebiO.YangH-. C.WarkianiM. E.HouJ.ChenV.2017Preparation of iridescent 2D photonic crystals by using a mussel-inspired spatial patterning of ZIF-8 with potential applications in optical switch and chemical sensor9(43):380763808010.1021/acsami.7b1361828990757Search in Google Scholar
Redmon, J. and Farhadi, A. 2018. YOLOv3: an incremental improvement. Computer Vision and Pattern Recognition 1.RedmonJ.FarhadiA.2018YOLOv3: an incremental improvement1Search in Google Scholar
Rogers, C. 2015. Dr. Craig Rogers: da Vinci Partial Nephrectomy, Vattikuti Foundation, Bangalore, Available at: https://www.youtube.com/watch?v=gdg7EhsKki8&ab_channel=VattikutiFoundation (Accessed March 20, 2021).RogersC.2015Vattikuti FoundationBangaloreAvailable at: https://www.youtube.com/watch?v=gdg7EhsKki8&ab_channel=VattikutiFoundation(AccessedMarch 20, 2021)Search in Google Scholar
Roth, H. R., Le, L., Liu, J., Yao, J., Seff, A., Cherry, K., Kim, L. and Summers, R. M. 2016. Improving computer-aided detection using convolutional neural networks and random view aggregation. IEEE Transactions on Medical Imaging 35(5): 1170–1181, doi: 10.1109/TMI.2015.2482920.RothH. R.LeL.LiuJ.YaoJ.SeffA.CherryK.KimL.SummersR. M.2016Improving computer-aided detection using convolutional neural networks and random view aggregation35(5):11701181doi:10.1109/TMI.2015.2482920734033426441412Open DOISearch in Google Scholar
Roy, S., Menapace, W., Oei, S., Luijten, B., Fini, E., Saltori, C., Huijben, I. A. M., Chennakeshava, N., Mento, F., Sentelli, A., Peschiera, E., Trevisan, R., Maschietto, G., Torri, E., Inchingolo, R., Smargiassi, A., Soldatti, G., Rota, P., Passerini, A., Sloun, R. J. G. V., Ricci, E. and Demi, L. 2020. Deep learning for classification and localization of COVID-19 markers in point-of-care lung ultrasound. IEEE Transactions on Medical Imaging 39(8): 2676–2687, doi: 10.1109/TMI.2020.2994459.RoyS.MenapaceW.OeiS.LuijtenB.FiniE.SaltoriC.HuijbenI. A. M.ChennakeshavaN.MentoF.SentelliA.PeschieraE.TrevisanR.MaschiettoG.TorriE.InchingoloR.SmargiassiA.SoldattiG.RotaP.PasseriniA.SlounR. J. G. V.RicciE.DemiL.2020Deep learning for classification and localization of COVID-19 markers in point-of-care lung ultrasound39(8):26762687doi:10.1109/TMI.2020.299445932406829Open DOISearch in Google Scholar
Seff, A., Cherry, K. M., Roth, H., Liu, J., Wang, S., Hoffman, J., Turkbey, E. B. and Summers, R. M. 2014. 2D view aggregation for lymph node detection using a shallow hierarchy of linear classifiers. Medical Image Computing and Computer-Assisted Interval 17(Pt 1): 544–552, doi: 10.1007/978-3-319-10404-1_68.SeffA.CherryK. M.RothH.LiuJ.WangS.HoffmanJ.TurkbeyE. B.SummersR. M.20142D view aggregation for lymph node detection using a shallow hierarchy of linear classifiers17(Pt 1):544552doi:10.1007/978-3-319-10404-1_68435091125333161Open DOISearch in Google Scholar
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D. and Batra, D. 2017. Grad-CAM: visual explanations from deep networks via gradient-based localization. 2017 IEEE International Conference on Computer Vision (ICCV), October 22–29, pp. 618–626, doi: 10.1109/ICCV.2017.74.SelvarajuR. R.CogswellM.DasA.VedantamR.ParikhD.BatraD.2017Grad-CAM: visual explanations from deep networks via gradient-based localizationOctober22–29pp.618626doi:10.1109/ICCV.2017.74Open DOISearch in Google Scholar
Shin, H-. C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D. and Summers, R. M. 2016. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Transactions on Medical Imaging 35(5): 1285–1298, doi: 10.1109/TMI.2016.2528162.ShinH-. C.RothH. R.GaoM.LuL.XuZ.NoguesI.YaoJ.MolluraD.SummersR. M.2016Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning35(5):12851298doi:10.1109/TMI.2016.2528162489061626886976Open DOISearch in Google Scholar
Simonyan, K. and Zisserman, A. 2014. Very deep convolutional networks for large-scale image recognition.SimonyanK.ZissermanA.2014Search in Google Scholar
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhouchke, V. and Rabinovich, A. 2015. Going deeper with convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7–12, pp. 1–9, doi: 10.1109/CVPR.2015.7298594.SzegedyC.LiuW.JiaY.SermanetP.ReedS.AnguelovD.ErhanD.VanhouchkeV.RabinovichA.2015Going deeper with convolutionsJune7–12pp.19doi:10.1109/CVPR.2015.7298594Open DOISearch in Google Scholar
tzutalin. 2017. LabelImg, Available at: https://github.com/tzutalin/labelImg.tzutalin2017Available at: https://github.com/tzutalin/labelImgSearch in Google Scholar
Ünver, H. M. and Ayan, E. 2019. Skin lesion segmentation in dermoscopic images with combination of YOLO and GrabCut Algorithm. Diagnostics (Basel, Switzerland) 9(3), doi: 10.3390/diagnostics9030072.ÜnverH. M.AyanE.2019Skin lesion segmentation in dermoscopic images with combination of YOLO and GrabCut Algorithm9(3)doi:10.3390/diagnostics9030072678758131295856Open DOISearch in Google Scholar
Wang, Y., Zheng, B., Gao, D. and Wang, J. 2020. A weakly-supervised framework for COVID-19 classification and lesion localization from chest CT. IEEE Transactions on Medical Imaging 39(8): 2615–2625, doi: 10.1109/TMI.2020.2995965.WangY.ZhengB.GaoD.WangJ.2020A weakly-supervised framework for COVID-19 classification and lesion localization from chest CT39(8):26152625doi:10.1109/TMI.2020.299596533156775Open DOISearch in Google Scholar
Wang, Y., Zheng, B., Gao, D. and Wang, J. 2018. Fully convolutional neural networks for prostate cancer detection using multi-parametric magnetic resonance images: an initial investigation. 2018 24th International Conference on Pattern Recognition (ICPR), August 20–24, pp. 3814–3819, doi: 10.1109/ICPR.2018.8545754.WangY.ZhengB.GaoD.WangJ.2018Fully convolutional neural networks for prostate cancer detection using multi-parametric magnetic resonance images: an initial investigationAugust20–24pp.38143819doi:10.1109/ICPR.2018.8545754Open DOISearch in Google Scholar
Wu, Y. -H., Gao, S. -H., Mei, J., Xu, J., Fan, D. -P., Zhang, R. -G. and Cheng, M. -M. 2021. JCS: an explainable COVID-19 diagnosis system by joint classification and segmentation 30: 3113–3126.WuY. -H.GaoS. -H.MeiJ.XuJ.FanD. -P.ZhangR. -G.ChengM. -M.2021303113312610.1109/TIP.2021.305878333600316Search in Google Scholar
Zeiler, M. D., Fergus, R. 2014. “Visualizing and understanding convolutional networks”, In Fleet, D., Pajdla, T., Schiele, B. and Tuytelaars, T. (Eds), Computer Vision – ECCV 2014 Cham: Springer International Publishing, pp. 818–833.ZeilerM. D.FergusR.2014“Visualizing and understanding convolutional networks”, InFleetD.PajdlaT.SchieleB.TuytelaarsT.(Eds)ChamSpringer International Publishingpp.81883310.1007/978-3-319-10590-1_53Search in Google Scholar
Zhang, H., Cisse, M., Dauphin, Y. N. and Lopezpaz, D. 2018. Mixup: Beyond empirical risk minimization. presented at the in Proc. Int. Conf. Learn. Represent.ZhangH.CisseM.DauphinY. N.LopezpazD.2018Mixup: Beyond empirical risk minimizationSearch in Google Scholar