From the fact that the likelihood of curing melanoma increases when diagnosed early and treated timely, DeepDerma emerged as a project that aims at developing Deep Learning based solutions that aid in the early detection of melanoma, and that would act as a powerful tool for use in clinical practice.
Different factors participate in not diagnosing melanoma early enough
The clinical presentation of melanoma is complex and diverse
Sometimes it is hard to distinguish moles from cancer
Such melanoma subtypes are difficult to diagnose in a timely manner
A. Ali and T. Deserno, "A systematic review of automated melanoma detection in dermatoscopic images and its ground truth data," SPIE Medical Imaging, 2012, San Diego, California, United States, 2012.
A. Ali, J. Li, and T. Trappenberg, "Supervised versus unsupervised deep learning based methods for skin lesion segmentation in dermoscopy images," Canadian AI 2019, 2019.
A. Ali, J. Li, S.J., O'Shea, G. Yang, T. Trappenberg, and X. Ye, "A deep learning based approach to skin lesion border extraction with a novel edge detector in dermoscopy images," IEEE International Joint Conference on Neural Networks (IJCNN), 2019.
A. Ali, S.J., O'Shea, and J. Li, "Are neural networks effective in detecting melanoma using genomic data?," 16th Annual Joint GenoMel, BioGenoMel, MELGEN Scientific Meeting (Athens, Greece, April, 8-10 2019), 2019.
A. Ali, S.J., O'Shea, J. Li, and L.M., Roche, "Are neural networks effective in detecting melanoma using genomic data?," British Association of Dermatologists, 99th Annual Meeting (Liverpool, UK, July 2-4 2019), 2019.
We are always open for collaborations for the mission of fighting melanoma. Please let us know what you have in mind and we can take it from there.
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Meet the team that's working on fighting melanoma with deep learning!
I'm a PhD candidate at the University of Stirling (part-time) working on the early detection of melanoma using machine learning and image processing techniques. To stay up-to-date on my work, you can kindly check my website abder.io, or follow me on Twitter @abderhasan!
Jingpeng Li is currently a Reader (equivalent to a full-professor without chair in the US) at the Division of Computer Science and Mathematics, University of Stirling, UK. He received the MSc Degree in Computational Mathematics from Huazhong University of Science and Technology (China) in 1998, and the PhD in Artificial Intelligence from University of Leeds (UK) in 2002. He joined the University of Bradford (UK) as a Research Assistant in 2003. He then worked at the University of Nottingham (both the UK and China campuses) as Research Fellow, Senior Research Fellow and Assistant Professor in 2004-2013. You can learn more about Professor Li from his webpage: http://www.cs.stir.ac.uk/~jli/
Guany Yang is currently an honorary lecturer with the Neuroscience Research Centre, Cardiovascular and Cell Sciences Institute, St. George’s, University of London. He is also an image processing physicist and honorary senior research fellow working at Cardiovascular Research Centre, Royal Brompton Hospital and also affiliate with National Heart and Lung Institute, Imperial College London. You can learn more about Guang from, here: https://www.imperial.ac.uk/people/g.yang
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