3 papers
eess.IV2026
Revealing Mammographic Phenotypes in Deep Learning Breast Cancer Risk Models
Ruiyu Jia, Yanqi Xu, Yuxuan Chen +2
Mammogram-based deep learning models have improved breast cancer risk prediction, but the learned imaging patterns remain underexplored. Existing interpretability methods rely on s…
cs.CV2026
Perturb-and-Restore: Simulation-driven Structural Augmentation Framework for Imbalance Chromosomal Anomaly Detection
Yilan Zhang, Hanbiao Chen, Changchun Yang +10
Detecting structural chromosomal abnormalities is crucial for accurate diagnosis and management of genetic disorders. However, collecting sufficient structural abnormality data is…
q-bio.GN2025
TransST: Transfer Learning Embedded Spatial Factor Modeling of Spatial Transcriptomics Data
Shuo Shuo Liu, Shikun Wang, Yuxuan Chen +3
Background: Spatial transcriptomics have emerged as a powerful tool in biomedical research because of its ability to capture both the spatial contexts and abundance of the complete…