4 citations · 7 across the 5 of their papers we have counts for
6 papers
Pitfalls of Administrative Censoring in Survival Models with Time-Indexed Inputs
Yanqi Xu, Hui Dai, Carlos Fernandez-Granda +2
Survival models can model time-to-event outcomes using partially observed data. They are widely used in clinical prediction, including cancer risk, disease progression, treatment r…
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…
External Validation of Deep Learning Models for BI-RADS Breast Density Prediction from Ultrasound Images
Yuxuan Chen, Arianna Bunnell, Yanqi Xu +4
We externally validated three deep learning models (DenseNet121, ViT-B/32, and ResNet50) for predicting mammographic breast density from breast ultrasound exams on an independent c…
Leveraging Fine-Tuned Large Language Models for Interpretable Pancreatic Cystic Lesion Feature Extraction and Risk Categorization
Ebrahim Rasromani, Stella K. Kang, Yanqi Xu +14
Background: Manual extraction of pancreatic cystic lesion (PCL) features from radiology reports is labor-intensive, limiting large-scale studies needed to advance PCL research. Pur…
A Multi-Modal AI System for Screening Mammography: Integrating 2D and 3D Imaging to Improve Breast Cancer Detection in a Prospective Clinical Study
Jungkyu Park, Jan Witowski, Yanqi Xu +8
Although digital breast tomosynthesis (DBT) improves diagnostic performance over full-field digital mammography (FFDM), false-positive recalls remain a concern in breast cancer scr…
Understanding differences in applying DETR to natural and medical images
Yanqi Xu, Yiqiu Shen, Carlos Fernandez-Granda +2
Transformer-based detectors have shown success in computer vision tasks with natural images. These models, exemplified by the Deformable DETR, are optimized through complex enginee…