Age-Related Eye Disease Study Research Group. 2001. The age-related eye disease study system for classifying age-related macular degeneration from stereoscopic color fundus photographs: The age-related eye disease study report number 6. American Journal of Ophthalmology. 132(5):668-681.
Amarasinghe H, Johnson N, Lalloo R, Kumaraarachchi M, Warnakulasuriya S. 2010. Derivation and validation of a risk-factor model for detection of oral potentially malignant disorders in populations with high prevalence. British Journal of Cancer. 103(3):303-309.
Burlina P, Pacheco KD, Joshi N, Freund DE, Bressler NM. 2017a. Comparing humans and deep learning performance for grading amd: A study in using universal deep features and transfer learning for automated amd analysis. Computers in Biology and Medicine. (82):80-86.
Burlina PM, Joshi N, Pacheco KD, Liu TA, Bressler NM. 2019. Assessment of deep generative models for high-resolution synthetic retinal image generation of age-related macular degeneration. JAMA Ophthalmology. 137(3):258-264.
Burlina PM, Joshi N, Pekala M, Pacheco KD, Freund DE, Bressler NM. 2017b. Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks. JAMA Ophthalmology. 135(11):1170-1176.
Crossman T, Warburton F, Richards MA, Smith H, Ramirez A, Forbes LJ. 2016. Role of general practice in the diagnosis of oral cancer. British Journal of Oral and Maxillofacial Surgery. 54(2):208-212.
Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. 2017. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 542(7639):115-118.
Ferreira PM, Mendonça T, Rozeira J, Rocha P. 2012. An annotation tool for dermoscopic image segmentation. Proceedings of the 1st International Workshop on Visual Interfaces for Ground Truth Collection in Computer Vision Applications; 2012.
Fiedler N, Bestmann M, Hendrich N. 2018. Imagetagger: An open source online platform for collaborative image labeling. Robot World Cup; 2018: Springer.
Globocan: Estimated cancer incidence, mortality and prevalance worldwide in 2018. 2018. Lyon:World Health Organisation,; [accessed 2020 3 April]. https://gco.iarc.fr/today/fact-sheets-populations.
Güneri P, Epstein JB. 2014. Late stage diagnosis of oral cancer: Components and possible solutions. Oral Oncology. 50(12):1131-1136.
Haron N, Zain RB, Ramanathan A, Abraham MT, Liew CS, Ng KG, Cheng LC, Husin RB, Chong SMY, Thangavalu LAP. 2020. M-health for early detection of oral cancer in low-and middle-income countries. Telemedicine and e-Health. 26(3):278-285.
Joda T, Yeung A, Hung K, Zitzmann N, Bornstein M. 2020. Disruptive innovation in dentistry: What it is and what could be next. Journal of Dental Research. 16:22034520978774.
Krig S. 2014. Ground truth data, content, metrics, and analysis. Computer vision metrics: Survey, taxonomy, and analysis. Berkeley, CA: Apress. p. 283-311.
Lindsell CJ, Stead WW, Johnson KB. 2020. Action-informed artificial intelligence—matching the algorithm to the problem. JAMA. 323(21):2141-2142.
Mendonça T, Ferreira PM, Marques JS, Marcal AR, Rozeira J. Ph 2-a dermoscopic image database for research and benchmarking. 2013. 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC); 2013: IEEE.
Nagao T, Warnakulasuriya S. 2020. Screening for oral cancer: Future prospects, research and policy development for asia. Oral Oncology. 105:104632.
Onizawa K, Nishihara K, Yamagata K, Yusa H, Yanagawa T, Yoshida H. 2003. Factors associated with diagnostic delay of oral squamous cell carcinoma. Oral Oncology. 39(8):781-788.
Scully C. 2012. Oral and maxillofacial medicine-e-book: The basis of diagnosis and treatment. 2nd ed. Edinburgh: Churchill Livingstone/Elsevier.
Syed Mohd Sobri SNS, Kanapathy J, Liew CS, Cheong SC. 2020. The establishment of the asia-pacific oral cancer network (APOCNET)-inaugural stakeholders' meeting. Oral Diseases. 26(5):1094-1097.
Tschandl P, Rosendahl C, Kittler H. 2018. The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific data. 5:180161.
Uthoff RD, Song B, Sunny S, Patrick S, Suresh A, Kolur T, Keerthi G, Spires O, Anbarani A, Wilder-Smith P. 2018. Point-of-care, smartphone-based, dual-modality, dual-view, oral cancer screening device with neural network classification for low-resource communities. PloS one. 13(12):e0207493.
Warnakulasuriya K, Ekanayake A, Sivayoham S, Stjernswärd J, Pindborg J, Sobin L, Perera K. 1984. Utilization of primary health care workers for early detection of oral cancer and precancer cases in sri lanka. Bulletin of the World Health Organization. 62(2):243.
Warnakulasuriya S. 2009. Global epidemiology of oral and oropharyngeal cancer. Oral Oncology. 45(4-5):309-316.
Warnakulasuriya S. 2018. Clinical features and presentation of oral potentially malignant disorders. Oral surgery, oral medicine, oral pathology and oral radiology. 125(6):582-590.
Welikala RA, Remagnino P, Lim JH, Chan CS, Rajendran S, Kallarakkal TG, Zain RB, Jayasinghe RD, Rimal J, Kerr AR et al. 2020a. Automated detection and classification of oral lesions using deep learning for early detection of oral cancer. IEEE Access. 8:132677-132693.
Welikala RA, Remagnino P, Lim JH, Chan CS, Rajendran S, Kallarakkal TG, Zain RB, Jayasinghe RD, Rimal J, Kerr AR et al. Fine-tuning deep learning architectures for early detection of oral cancer. 2020b. Mathematical and Computational Oncology: Second International Symposium, ISMCO 2020, San Diego, CA, USA, October 8–10, 2020, Proceedings; 2020b: Springer Nature.
Yamashita R, Nishio M, Do RKG, Togashi K. 2018. Convolutional neural networks: An overview and application in radiology. Insights into imaging. 9(4):611-629.
Zain RB, Ikeda N, Razak IA, Axéll T, Majid ZA, Gupta PC, Yaacob M. 1997. A national epidemiological survey of oral mucosal lesions in malaysia. Community dentistry and oral epidemiology. 25(5):377-383.