Mengjia Xu

This paper improves sickle cell disease morphology analysis by combining a YOLO detector with a DenseNet classifier for accurate detection and classification of red blood cell morphologies in full-scope microscopy images.

This study introduces a framework for automatically classifying circulating blood cell clusters using flow cytometry images, achieving over 95% accuracy in cluster classification and phenotype identification.

This paper introduces QCML, a quantum-inspired machine learning framework, to predict chromosomal instability from CTC morphology, achieving higher accuracy than conventional methods in high-dimensional biomedical data.

This study uses hyperbolic neural networks to analyze MEG brain networks from the Cam-CAN dataset, revealing age-related hierarchical changes in brain subnetworks.

This study uses hyperbolic graph embedding to analyze MEG brain networks in individuals with subjective cognitive decline, identifying hierarchical differences linked to cognitive impairment.

This paper quantifies functional brain connectomic changes in aMCI patients during cognitive training using a Gaussian embedding method, identifying key brain regions with significant variation.