33 citations · 160 across the 19 of their papers we have counts for
33 papers
Causal multi-modal AI for personalized chemosensitivity prediction
Dhruva Biswas, Jeroen Berrevoets, Alec McClean +31
Chemotherapy improves survival for some patients with breast cancer, but doctors cannot reliably predict who. Current guidelines rely on recurrence scores as a proxy for treatment…
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…
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…
Multi-modal AI for comprehensive breast cancer prognostication
Jan Witowski, Ken G. Zeng, Joseph Cappadona +48
Treatment selection in breast cancer is guided by molecular subtypes and clinical characteristics. However, current tools including genomic assays lack the accuracy required for op…
A training regime to learn unified representations from complementary breast imaging modalities
Umang Sharma, Jungkyu Park, Laura Heacock +2
Full Field Digital Mammograms (FFDMs) and Digital Breast Tomosynthesis (DBT) are the two most widely used imaging modalities for breast cancer screening. Although DBT has increased…
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…