AI-based detection of erythema migrans and disambiguation against other skin lesions

[1]  R. C. Johnson,et al.  Failure of treatment with cephalexin for Lyme disease. , 2000, Archives of family medicine.

[2]  A. Steere,et al.  Clinical Characteristics and Treatment Outcome of Early Lyme Disease in Patients with Microbiologically Confirmed Erythema Migrans , 2002, Annals of Internal Medicine.

[3]  A. Steere,et al.  The presenting manifestations of Lyme disease and the outcomes of treatment. , 2003, The New England journal of medicine.

[4]  D. Lipsker,et al.  How accurate is a clinical diagnosis of erythema chronicum migrans? Prospective study comparing the diagnostic accuracy of general practitioners and dermatologists in an area where lyme borreliosis is endemic. , 2004, Archives of dermatology.

[5]  J. Halperin,et al.  The clinical assessment, treatment, and prevention of lyme disease, human granulocytic anaplasmosis, and babesiosis: clinical practice guidelines by the Infectious Diseases Society of America. , 2006, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[6]  Tsung-Hua Li,et al.  Erythema migrans mimicking cervical cellulitis with deep neck infection in a child with Lyme disease. , 2007, Journal of the Formosan Medical Association = Taiwan yi zhi.

[7]  C. Tibbles,et al.  Does this patient have erythema migrans? , 2007, JAMA.

[8]  R. Müllegger,et al.  Skin Manifestations of Lyme Borreliosis , 2008, American journal of clinical dermatology.

[9]  Philippe Burlina,et al.  Automatic screening of age-related macular degeneration and retinal abnormalities , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[10]  C. Bhate,et al.  Lyme disease: Part I. Advances and perspectives. , 2011, Journal of the American Academy of Dermatology.

[11]  K. Kortte,et al.  Bull's-Eye and Nontarget Skin Lesions of Lyme Disease: An Internet Survey of Identification of Erythema Migrans , 2012, Dermatology research and practice.

[12]  F. Strle,et al.  Lyme borreliosis , 2012, The Lancet.

[13]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[14]  P. Burlina,et al.  Automated classification of severity of age-related macular degeneration from fundus photographs. , 2013, Investigative ophthalmology & visual science.

[15]  B. Kuehn.  CDC estimates 300,000 US cases of Lyme disease annually. , 2013, JAMA.

[16]  Vera Maraspin Carman,et al.  Supervised Visual System for Recognition of Erythema Migrans, an Early Skin Manifestation of Lyme Borreliosis , 2014 .

[17]  A. Hinckley,et al.  Lyme disease testing by large commercial laboratories in the United States. , 2014, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[18]  E. Shapiro.  Clinical practice. Lyme disease. , 2014, The New England journal of medicine.

[19]  Philippe Burlina,et al.  Automated segmentation of geographic atrophy of the retinal epithelium via random forests in AREDS color fundus images , 2015, Comput. Biol. Medicine.

[20]  D. Mazori,et al.  Vesicular erythema migrans: an atypical and easily misdiagnosed form of Lyme disease. , 2015, Dermatology online journal.

[21]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[22]  R. Nadelman.  Erythema migrans. , 2015, Infectious disease clinics of North America.

[23]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Serge J. Belongie,et al.  Residual Networks Behave Like Ensembles of Relatively Shallow Networks , 2016, NIPS.

[25]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  A. Steere,et al.  Lyme borreliosis. , 2016, Nature reviews. Disease primers.

[27]  Philippe Burlina,et al.  Comparing humans and deep learning performance for grading AMD: A study in using universal deep features and transfer learning for automated AMD analysis , 2017, Comput. Biol. Medicine.

[28]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[29]  Neil J. Joshi,et al.  Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods , 2017, PloS one.

[30]  Sergey Ioffe,et al.  Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.

[31]  Neil J. Joshi,et al.  Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks , 2017, JAMA ophthalmology.

[32]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[33]  Hao Chen,et al.  Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks , 2017, IEEE Transactions on Medical Imaging.

[34]  Neil J. Joshi,et al.  Utility of Deep Learning Methods for Referability Classification of Age-Related Macular Degeneration , 2018, JAMA ophthalmology.

[35]  Neil J. Joshi,et al.  Use of Deep Learning for Detailed Severity Characterization and Estimation of 5-Year Risk Among Patients With Age-Related Macular Degeneration , 2018, JAMA ophthalmology.

[36]  Y. Fujisawa,et al.  Deep‐learning‐based, computer‐aided classifier developed with a small dataset of clinical images surpasses board‐certified dermatologists in skin tumour diagnosis , 2018, The British journal of dermatology.

[37]  Guang Yang,et al.  Automatic skin lesion segmentation by coupling deep fully convolutional networks and shallow network with textons , 2019, Journal of medical imaging.

[38]  Philippe Burlina,et al.  Automated detection of erythema migrans and other confounding skin lesions via deep learning , 2019, Comput. Biol. Medicine.

[39]  Solomon Saul,et al.  Rash, Radiculopathy, and Cognitive Biases. , 2019, Aerospace medicine and human performance.

[40]  B. Pritt,et al.  The Lyme Disease Biobank: Characterization of 550 Patient and Control Samples from the East Coast and Upper Midwest of the United States , 2020, Journal of Clinical Microbiology.

[41]  Asifullah Khan,et al.  A survey of the recent architectures of deep convolutional neural networks , 2019, Artificial Intelligence Review.

[42]  Rainer Hofmann-Wellenhof,et al.  A deep learning system for differential diagnosis of skin diseases , 2019, Nature Medicine.