Mengran Fan, Digital pathology AI and radiomics model development

Mengran Fan is an early-career postdoctoral researcher funded by the DART project and working with Prof. Jens Rittscher and Dr Tapabrata (Rohan) Chakraborty on Digital Pathology AI and Radiomics Model Development and Validation (WP4). Her research focuses on interpretable machine learning methods to transform high-resolution biomedical imaging data into meaningful quantitative information with clinical validity.

Mengran works on digital pathology and radiomics modelling under the supervision of Prof. Jens Rittscher and Dr Tapabrata (Rohan) Chakraborty, contributing to the processing of relevant lung radiology/histopathology images and the design of new algorithms to meet the clinical needs of lung cancer diagnosis.

Before joining the DART project, Mengran received her Bachelor’s degree in Computer Science from the University of Nottingham and completed her PhD (DPhil) in Biomedical Image Analysis at the University of Oxford. Her PhD (DPhil) thesis focused on attention-based, interpretable fine-grained feature learning for microscopy and histopathology images that can jointly capture local high-resolution detail and global contextual information. This research resulted in several peer-reviewed publications and was awarded a Graduate Fellowship at Trinity College, Oxford.

While pursuing her doctoral studies, she participated in an international internship program at Microsoft Research Asia and spent three months working at Peking Union Medical College Hospital. The project resulted in a new fine-grained medical image-based classification algorithm and a new clinical mycology dataset, which was the first attempt to bring deep learning-based methods to fungal species identification. The findings were published in a peer-reviewed article and were presented in two international conferences.

Mengran Fan works on Digital pathology AI & radiomics model development (DART’s Work Package 4).