1 citations · 1 across the 5 of their papers we have counts for
5 papers
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…
Evaluating Generative AI as an Educational Tool for Radiology Resident Report Drafting
Antonio Verdone, Aidan Cardall, Fardeen Siddiqui +10
Objective: Radiology residents require timely, personalized feedback to develop accurate image analysis and reporting skills. Increasing clinical workload often limits attendings'…
Fine-Tuning In-House Large Language Models to Infer Differential Diagnosis from Radiology Reports
Luoyao Chen, Revant Teotia, Antonio Verdone +4
Radiology reports summarize key findings and differential diagnoses derived from medical imaging examinations. The extraction of differential diagnoses is crucial for downstream ta…
BURExtract-Llama: An LLM for Clinical Concept Extraction in Breast Ultrasound Reports
Yuxuan Chen, Haoyan Yang, Hengkai Pan +6
Breast ultrasound is essential for detecting and diagnosing abnormalities, with radiology reports summarizing key findings like lesion characteristics and malignancy assessments. E…