Published on 17.10.18 in Vol 20, No 10 (2018): October
Works citing "Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review"
According to Crossref, the following articles are citing this article (DOI 10.2196/11936):
(note that this is only a small subset of citations)
- Reshma M, Shan PB. A Clinical Decision Support System for Micro panoramic Melanoma Detection and grading using soft computing Technique. Measurement 2020;:108024CrossRef
- Han SS, Park I, Eun Chang S, Lim W, Kim MS, Park GH, Chae JB, Huh CH, Na J. Augmented Intelligence Dermatology: Deep Neural Networks Empower Medical Professionals in Diagnosing Skin Cancer and Predicting Treatment Options for 134 Skin Disorders. Journal of Investigative Dermatology 2020;CrossRef
- Nakamura I. Phase diagrams of polymer-containing liquid mixtures with a theory-embedded neural network. New Journal of Physics 2020;22(1):015001CrossRef
- Mahbod A, Schaefer G, Wang C, Dorffner G, Ecker R, Ellinger I. Transfer learning using a multi-scale and multi-network ensemble for skin lesion classification. Computer Methods and Programs in Biomedicine 2020;193:105475CrossRef
- Canales C, Lee C, Cannesson M. Science Without Conscience Is but the Ruin of the Soul. Anesthesia & Analgesia 2020;130(5):1234CrossRef
- Bajwa MN, Muta K, Malik MI, Siddiqui SA, Braun SA, Homey B, Dengel A, Ahmed S. Computer-Aided Diagnosis of Skin Diseases Using Deep Neural Networks. Applied Sciences 2020;10(7):2488CrossRef
- Cullell-Dalmau M, Otero-Viñas M, Manzo C. Research Techniques Made Simple: Deep Learning for the Classification of Dermatological Images. Journal of Investigative Dermatology 2020;140(3):507CrossRef
- Iglesias-Puzas , Boixeda P. Deep learning y DerMATología. Actas Dermo-Sifiliográficas 2020;111(3):192CrossRef
- Dzobo K, Adotey S, Thomford NE, Dzobo W. Integrating Artificial and Human Intelligence: A Partnership for Responsible Innovation in Biomedical Engineering and Medicine. OMICS: A Journal of Integrative Biology 2020;24(5):247CrossRef
- Iglesias-Puzas , Boixeda P. Deep Learning and Mathematical Models in Dermatology. Actas Dermo-Sifiliográficas (English Edition) 2020;111(3):192CrossRef
- Kadampur MA, Al Riyaee S. Skin cancer detection: Applying a deep learning based model driven architecture in the cloud for classifying dermal cell images. Informatics in Medicine Unlocked 2020;18:100282CrossRef
- Lopez-Jimenez F, Attia Z, Arruda-Olson AM, Carter R, Chareonthaitawee P, Jouni H, Kapa S, Lerman A, Luong C, Medina-Inojosa JR, Noseworthy PA, Pellikka PA, Redfield MM, Roger VL, Sandhu GS, Senecal C, Friedman PA. Artificial Intelligence in Cardiology: Present and Future. Mayo Clinic Proceedings 2020;95(5):1015CrossRef
- Hogarty DT, Su JC, Phan K, Attia M, Hossny M, Nahavandi S, Lenane P, Moloney FJ, Yazdabadi A. Artificial Intelligence in Dermatology—Where We Are and the Way to the Future: A Review. American Journal of Clinical Dermatology 2020;21(1):41CrossRef
- Balaji V, Suganthi S, Rajadevi R, Krishna Kumar V, Saravana Balaji B, Pandiyan S. Skin disease detection and segmentation using dynamic graph cut algorithm and classification through Naive Bayes Classifier. Measurement 2020;:107922CrossRef
- Pacheco AG, Krohling RA. The impact of patient clinical information on automated skin cancer detection. Computers in Biology and Medicine 2020;116:103545CrossRef
- Heidari AE, Pham TT, Ifegwu I, Burwell R, Armstrong WB, Tjoson T, Whyte S, Giorgioni C, Wang B, Wong BJF, Chen Z. The use of optical coherence tomography and convolutional neural networks to distinguish normal and abnormal oral mucosa. Journal of Biophotonics 2020;13(3)CrossRef
- Mont MA, Krebs VE, Backstein DJ, Browne JA, Mason JB, Taunton MJ, Callaghan JJ. Artificial Intelligence: Influencing Our Lives in Joint Arthroplasty. The Journal of Arthroplasty 2019;34(10):2199CrossRef
- Brinker TJ, Hekler A, Enk AH, Klode J, Hauschild A, Berking C, Schilling B, Haferkamp S, Schadendorf D, Fröhling S, Utikal JS, von Kalle C, Ludwig-Peitsch W, Sirokay J, Heinzerling L, Albrecht M, Baratella K, Bischof L, Chorti E, Dith A, Drusio C, Giese N, Gratsias E, Griewank K, Hallasch S, Hanhart Z, Herz S, Hohaus K, Jansen P, Jockenhöfer F, Kanaki T, Knispel S, Leonhard K, Martaki A, Matei L, Matull J, Olischewski A, Petri M, Placke J, Raub S, Salva K, Schlott S, Sody E, Steingrube N, Stoffels I, Ugurel S, Sondermann W, Zaremba A, Gebhardt C, Booken N, Christolouka M, Buder-Bakhaya K, Bokor-Billmann T, Enk A, Gholam P, Hänßle H, Salzmann M, Schäfer S, Schäkel K, Schank T, Bohne A, Deffaa S, Drerup K, Egberts F, Erkens A, Ewald B, Falkvoll S, Gerdes S, Harde V, Hauschild A, Jost M, Kosova K, Messinger L, Metzner M, Morrison K, Motamedi R, Pinczker A, Rosenthal A, Scheller N, Schwarz T, Stölzl D, Thielking F, Tomaschewski E, Wehkamp U, Weichenthal M, Wiedow O, Bär CM, Bender-Säbelkampf S, Horbrügger M, Karoglan A, Kraas L, Faulhaber J, Geraud C, Guo Z, Koch P, Linke M, Maurier N, Müller V, Thomas B, Utikal JS, Alamri ASM, Baczako A, Berking C, Betke M, Haas C, Hartmann D, Heppt MV, Kilian K, Krammer S, Lapczynski NL, Mastnik S, Nasifoglu S, Ruini C, Sattler E, Schlaak M, Wolff H, Achatz B, Bergbreiter A, Drexler K, Ettinger M, Haferkamp S, Halupczok A, Hegemann M, Dinauer V, Maagk M, Mickler M, Philipp B, Wilm A, Wittmann C, Gesierich A, Glutsch V, Kahlert K, Kerstan A, Schilling B, Schrüfer P. A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task. European Journal of Cancer 2019;111:148CrossRef
- Brinker TJ, Hekler A, Enk AH, Klode J, Hauschild A, Berking C, Schilling B, Haferkamp S, Schadendorf D, Holland-Letz T, Utikal JS, von Kalle C, Ludwig-Peitsch W, Sirokay J, Heinzerling L, Albrecht M, Baratella K, Bischof L, Chorti E, Dith A, Drusio C, Giese N, Gratsias E, Griewank K, Hallasch S, Hanhart Z, Herz S, Hohaus K, Jansen P, Jockenhöfer F, Kanaki T, Knispel S, Leonhard K, Martaki A, Matei L, Matull J, Olischewski A, Petri M, Placke J, Raub S, Salva K, Schlott S, Sody E, Steingrube N, Stoffels I, Ugurel S, Zaremba A, Gebhardt C, Booken N, Christolouka M, Buder-Bakhaya K, Bokor-Billmann T, Enk A, Gholam P, Hänßle H, Salzmann M, Schäfer S, Schäkel K, Schank T, Bohne A, Deffaa S, Drerup K, Egberts F, Erkens A, Ewald B, Falkvoll S, Gerdes S, Harde V, Hauschild A, Jost M, Kosova K, Messinger L, Metzner M, Morrison K, Motamedi R, Pinczker A, Rosenthal A, Scheller N, Schwarz T, Stölzl D, Thielking F, Tomaschewski E, Wehkamp U, Weichenthal M, Wiedow O, Bär CM, Bender-Säbelkampf S, Horbrügger M, Karoglan A, Kraas L, Faulhaber J, Geraud C, Guo Z, Koch P, Linke M, Maurier N, Müller V, Thomas B, Utikal JS, Alamri ASM, Baczako A, Berking C, Betke M, Haas C, Hartmann D, Heppt MV, Kilian K, Krammer S, Lapczynski NL, Mastnik S, Nasifoglu S, Ruini C, Sattler E, Schlaak M, Wolff H, Achatz B, Bergbreiter A, Drexler K, Ettinger M, Haferkamp S, Halupczok A, Hegemann M, Dinauer V, Maagk M, Mickler M, Philipp B, Wilm A, Wittmann C, Gesierich A, Glutsch V, Kahlert K, Kerstan A, Schilling B, Schrüfer P. Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal of Cancer 2019;113:47CrossRef
- Brinker TJ, Hekler A, Hauschild A, Berking C, Schilling B, Enk AH, Haferkamp S, Karoglan A, von Kalle C, Weichenthal M, Sattler E, Schadendorf D, Gaiser MR, Klode J, Utikal JS. Comparing artificial intelligence algorithms to 157 German dermatologists: the melanoma classification benchmark. European Journal of Cancer 2019;111:30CrossRef
- Winkler JK, Fink C, Toberer F, Enk A, Hänßle HA. Melanomdiagnose mithilfe künstlicher Intelligenz. hautnah dermatologie 2019;35(2):38CrossRef
- Hekler A, Utikal JS, Enk AH, Solass W, Schmitt M, Klode J, Schadendorf D, Sondermann W, Franklin C, Bestvater F, Flaig MJ, Krahl D, von Kalle C, Fröhling S, Brinker TJ. Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images. European Journal of Cancer 2019;118:91CrossRef
- Hekler A, Utikal JS, Enk AH, Hauschild A, Weichenthal M, Maron RC, Berking C, Haferkamp S, Klode J, Schadendorf D, Schilling B, Holland-Letz T, Izar B, von Kalle C, Fröhling S, Brinker TJ, Schmitt L, Peitsch WK, Hoffmann F, Becker JC, Drusio C, Jansen P, Klode J, Lodde G, Sammet S, Schadendorf D, Sondermann W, Ugurel S, Zader J, Enk A, Salzmann M, Schäfer S, Schäkel K, Winkler J, Wölbing P, Asper H, Bohne A, Brown V, Burba B, Deffaa S, Dietrich C, Dietrich M, Drerup KA, Egberts F, Erkens A, Greven S, Harde V, Jost M, Kaeding M, Kosova K, Lischner S, Maagk M, Messinger AL, Metzner M, Motamedi R, Rosenthal A, Seidl U, Stemmermann J, Torz K, Velez JG, Haiduk J, Alter M, Bär C, Bergenthal P, Gerlach A, Holtorf C, Karoglan A, Kindermann S, Kraas L, Felcht M, Gaiser MR, Klemke C, Kurzen H, Leibing T, Müller V, Reinhard RR, Utikal J, Winter F, Berking C, Eicher L, Hartmann D, Heppt M, Kilian K, Krammer S, Lill D, Niesert A, Oppel E, Sattler E, Senner S, Wallmichrath J, Wolff H, Gesierich A, Giner T, Glutsch V, Kerstan A, Presser D, Schrüfer P, Schummer P, Stolze I, Weber J, Drexler K, Haferkamp S, Mickler M, Stauner CT, Thiem A. Superior skin cancer classification by the combination of human and artificial intelligence. European Journal of Cancer 2019;120:114CrossRef
- Ashfaq M, Minallah N, Ullah Z, Ahmad AM, Saeed A, Hafeez A. Performance Analysis of Low-Level and High-Level Intuitive Features for Melanoma Detection. Electronics 2019;8(6):672CrossRef
- Reiter O, Rotemberg V, Kose K, Halpern AC. Artificial Intelligence in Skin Cancer. Current Dermatology Reports 2019;8(3):133CrossRef
- Brinker TJ, Hekler A, Enk AH, von Kalle C, Huynh DQ. Enhanced classifier training to improve precision of a convolutional neural network to identify images of skin lesions. PLOS ONE 2019;14(6):e0218713CrossRef
- Maron RC, Weichenthal M, Utikal JS, Hekler A, Berking C, Hauschild A, Enk AH, Haferkamp S, Klode J, Schadendorf D, Jansen P, Holland-Letz T, Schilling B, von Kalle C, Fröhling S, Gaiser MR, Hartmann D, Gesierich A, Kähler KC, Wehkamp U, Karoglan A, Bär C, Brinker TJ, Schmitt L, Peitsch WK, Hoffmann F, Becker JC, Drusio C, Jansen P, Klode J, Lodde G, Sammet S, Schadendorf D, Sondermann W, Ugurel S, Zader J, Enk A, Salzmann M, Schäfer S, Schäkel K, Winkler J, Wölbing P, Asper H, Bohne A, Brown V, Burba B, Deffaa S, Dietrich C, Dietrich M, Drerup KA, Egberts F, Erkens A, Greven S, Harde V, Jost M, Kaeding M, Kosova K, Lischner S, Maagk M, Messinger AL, Metzner M, Motamedi R, Rosenthal A, Seidl U, Stemmermann J, Torz K, Velez JG, Haiduk J, Alter M, Bär C, Bergenthal P, Gerlach A, Holtorf C, Karoglan A, Kindermann S, Kraas L, Felcht M, Gaiser MR, Klemke C, Kurzen H, Leibing T, Müller V, Reinhard RR, Utikal J, Winter F, Berking C, Eicher L, Hartmann D, Heppt M, Kilian K, Krammer S, Lill D, Niesert A, Oppel E, Sattler E, Senner S, Wallmichrath J, Wolff H, Giner T, Glutsch V, Kerstan A, Presser D, Schrüfer P, Schummer P, Stolze I, Weber J, Drexler K, Haferkamp S, Mickler M, Stauner CT, Thiem A. Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks. European Journal of Cancer 2019;119:57CrossRef
- de Carvalho TM, Noels E, Wakkee M, Udrea A, Nijsten T. Development of Smartphone Apps for Skin Cancer Risk Assessment: Progress and Promise. JMIR Dermatology 2019;2(1):e13376CrossRef
- Hekler A, Utikal JS, Enk AH, Berking C, Klode J, Schadendorf D, Jansen P, Franklin C, Holland-Letz T, Krahl D, von Kalle C, Fröhling S, Brinker TJ. Pathologist-level classification of histopathological melanoma images with deep neural networks. European Journal of Cancer 2019;115:79CrossRef
- Winkler JK, Fink C, Toberer F, Enk A, Deinlein T, Hofmann-Wellenhof R, Thomas L, Lallas A, Blum A, Stolz W, Haenssle HA. Association Between Surgical Skin Markings in Dermoscopic Images and Diagnostic Performance of a Deep Learning Convolutional Neural Network for Melanoma Recognition. JAMA Dermatology 2019;155(10):1135CrossRef
- Peine A, Hallawa A, Schöffski O, Dartmann G, Fazlic LB, Schmeink A, Marx G, Martin L. A Deep Learning Approach for Managing Medical Consumable Materials in Intensive Care Units via Convolutional Neural Networks: Technical Proof-of-Concept Study. JMIR Medical Informatics 2019;7(4):e14806CrossRef
- Triantafyllidis AK, Tsanas A. Applications of Machine Learning in Real-Life Digital Health Interventions: Review of the Literature. Journal of Medical Internet Research 2019;21(4):e12286CrossRef
- Shen J, Zhang CJP, Jiang B, Chen J, Song J, Liu Z, He Z, Wong SY, Fang P, Ming W. Artificial Intelligence Versus Clinicians in Disease Diagnosis: Systematic Review. JMIR Medical Informatics 2019;7(3):e10010CrossRef
According to Crossref, the following books are citing this article (DOI 10.2196/11936)
:- Singh N, Kaur A. Interdisciplinary Approaches to Altering Neurodevelopmental Disorders. 2020. chapter 13:213CrossRef
- Sivasubramanian K, Xing L. LED-Based Photoacoustic Imaging. 2020. Chapter 9:203CrossRef
- Martorell-Marugán J, Tabik S, Benhammou Y, del Val C, Zwir I, Herrera F, Carmona-Sáez P. Computational Biology. 2019. :37CrossRef
- El-khatib H, Popescu D, Ichim L. Advances in Computational Intelligence. 2019. Chapter 32:377CrossRef
- Young K, Booth G, Simpson B, Dutton R, Shrapnel S. Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support. 2019. Chapter 6:48CrossRef
- Aldwgeri A, Abubacker NF. Advances in Visual Informatics. 2019. Chapter 20:214CrossRef