Siamese Neural Networks for Skin Cancer Classification and New Class Detection using Clinical and Dermoscopic Image Datasets (2022)
Conference Proceeding
Battle, M. L., Atapour-Abarghouei, A., & McGough, A. S. (2023). Siamese Neural Networks for Skin Cancer Classification and New Class Detection using Clinical and Dermoscopic Image Datasets. . https://doi.org/10.1109/bigdata55660.2022.10020820

Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification (2022)
Conference Proceeding
Bevan, P., & Atapour-Abarghouei, A. (2022). Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification. In K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvari, G. Niu, & S. Sabato (Eds.), Proceedings of Machine Learning Research (1874-1892)

Convolutional Neural Networks have demonstrated dermatologist-level performance in the classification of melanoma and other skin lesions, but prediction irregularities due to biases seen within the training data are an issue that should be addressed... Read More about Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification.

A Feasibility Study on Image Inpainting for Non-cleft Lip Generation from Patients with Cleft Lip (2022)
Conference Proceeding
Chen, S., Atapour-Abarghouei, A., Kerby, J., Ho, E. S., Sainsbury, D. C., Butterworth, S., & Shum, H. P. (2022). A Feasibility Study on Image Inpainting for Non-cleft Lip Generation from Patients with Cleft Lip. . https://doi.org/10.1109/bhi56158.2022.9926917

A Cleft lip is a congenital abnormality requiring surgical repair by a specialist. The surgeon must have extensive experience and theoretical knowledge to perform surgery, and Artificial Intelligence (AI) method has been proposed to guide surgeons in... Read More about A Feasibility Study on Image Inpainting for Non-cleft Lip Generation from Patients with Cleft Lip.

Detecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classification (2022)
Conference Proceeding
Bevan, P. J., & Atapour-Abarghouei, A. (2022). Detecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classification. In K. Kamnitsas, L. Koch, M. Islam, Z. Xu, J. Cardoso, Q. Doi, …S. Tsaftaris (Eds.), DART 2022: Domain Adaptation and Representation Transfer (1-11). https://doi.org/10.1007/978-3-031-16852-9_1

Convolutional Neural Networks have demonstrated human-level performance in the classification of melanoma and other skin lesions, but evident performance disparities between differing skin tones should be addressed before widespread deployment. In th... Read More about Detecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classification.

Long-term Reproducibility for Neural Architecture Search (2022)
Conference Proceeding
Towers, D., Forshaw, M., Atapour-Abarghouei, A., & McGough, A. S. (in press). Long-term Reproducibility for Neural Architecture Search.