Qiang is a postdoctoral researcher in the Translational Healthcare Technologies (THT) group. He is always passionate about applying his expertise in computer science to solve real world problems. Within the THT Group he is a lead research software engineer, focusing on computational methodologies for biomedical image processing. He also leads the investigation of machine/deep learning technologies to large-scale datasets on human lung tissue collected by a state-of-the-art fluorescence lifetime imaging microscopy (FLIM), so that the information retrieved is maximised for better bedside decisions by clinicians and more precise treatments for patients.
Deep Learning-Assisted Co-registration of Full-Spectral Autofluorescence Lifetime Microscopic Images with H&E-Stained Histology Images (2022).
Wang Q, Fernandes S, Williams G, Finlayson N, Akram A R, Dhaliwal K, Hopgood J R, Vallejo M.
Full spectrum fluorescence lifetime imaging with 0.5 nm spectral and 50 ps temporal resolution (2021)
Williams GOS, Williams E, Finlayson N, Erdogan AT, Wang Q, Fernandes S, Akram AR, Dhaliwal K, Henderson RK, Girkin JM, Bradley M.
Tissue proteomic analysis identifies mechanisms and stages of immunopathology in fatal COVID-19 (2021)
Russell C D, Valanciute A, Gachanja N, Stephen J, Penrice-Randal R, Armstrong S D, Clohisey S, Wang B, Al Qsous W, Wallace W, Oniscu G, Stevens J, Harrison D J, Dhaliwal K, Hiscox J A, Baillie J K, Akram A R, David D, Lucas C D.
Deep Learning in ex-vivo Lung Cancer Discrimination using Fluorescence Lifetime Endomicroscopic Images (2020).
Wang Q, Hopgood JR, Finlayson N, Williams GO, Fernandes S, Williams E, Akram A, Dhaliwal K, Vallejo M.