Yong En (Janet) Kok

Contact

• workRoom B86 School of Computer Science
  Jubilee Campus
  Wollaton Road
  Nottingham
  NG8 1BB
  UK
• Yong.Kok@nottingham.ac.uk

Biography

Yong En (Janet) Kok is a Computer Science PhD student at the University of Nottingham. She is part of the InLightenUs team and is a member of the Computer Vision Lab based in the School of Computer Science, working under the supervision of Dr Michael Pound and Dr Andrew Parkes. Her role in the InLightenUs team is to develop deep learning techniques to deal with aberrated microsopy image dataset. Prior to her PhD studies, she received a BSc(Hons) in Computer Science with Artificial Intelligence (First Class) from the University of Nottingham. Her early research concentrated on handling the label-imbalanced problem on medical dataset (lesions on CT images) and molecule chirality on line drawings of molecules.

Recent Publications

• YONG EN KOK, SIMON WOODWARD, ENDER ÖZCAN and MERCEDES TORRES TORRES, 2022. Molecular Informatics Identifying Chirality in Line Drawings of Molecules using Imbalanced Dataset Sampler for a Multilabel Classification Task.
• KOK, YONG EN, PSZCZOLKOWSKI, STEFAN, LAW, ZHE KANG, ALI, AZLINAWATI, KRISHNAN, KAILASH, BATH, PHILIP M, SPRIGG, NIKOLA, DINEEN, ROBERT A and FRENCH, ANDREW P, 2022. Semantic Segmentation of Spontaneous Intracerebral Hemorrhage, Intraventricular Hemorrhage, and Associated Edema on CT Images Using Deep Learning. Radiology. Artificial intelligence. 4(6), e220096

• View all publications

• YONG EN KOK, SIMON WOODWARD, ENDER ÖZCAN and MERCEDES TORRES TORRES, 2022. Molecular Informatics Identifying Chirality in Line Drawings of Molecules using Imbalanced Dataset Sampler for a Multilabel Classification Task.
• KOK, YONG EN, PSZCZOLKOWSKI, STEFAN, LAW, ZHE KANG, ALI, AZLINAWATI, KRISHNAN, KAILASH, BATH, PHILIP M, SPRIGG, NIKOLA, DINEEN, ROBERT A and FRENCH, ANDREW P, 2022. Semantic Segmentation of Spontaneous Intracerebral Hemorrhage, Intraventricular Hemorrhage, and Associated Edema on CT Images Using Deep Learning. Radiology. Artificial intelligence. 4(6), e220096