Manuscript
In Submission
Imaging Biomarkers Predicting Progression of Interstitial Lung Abnormalities to Idiopathic Pulmonary Fibrosis: Multicontinental Validation Study
Masha Bondarenko
I graduated from UC Berkeley with a degree in Electrical Engineering & Computer Science in 2024. I currently work in the Sohn Lab at UCSF, where I develop machine learning methods to address clinical challenges in radiology, with a focus on imaging-based risk stratification and multimodal data integration. I have a technical interest in vision foundation models, vision transformers, and diffusion/GANs.
Research
Manuscript
In Submission
Investigating AI's potential in risk-stratifying lung cancer screenings for future follow-ups: A retrospective analysis of a tertiary hospital cohort
Manuscript
Accepted
2026
Pre-imaging Predictors of Cardiac MR Image Quality using Large Language Model-Enabled Data Extraction
Manuscript
In Submission
Planner-Executor Style Multimodal Agentic System to Answer Patient Questions in Lung Cancer Screening CT
Large Language Models in Radiologic Numerical Tasks: A Thorough Evaluation and Error Analysis
Pre-imaging Predictors of Cardiac MR Image Quality with Large Language Model Based Data Extraction
Detection of Interstitial Lung Abnormalities on Chest CT: Comparative Evaluation of Radiomics and Deep Learning Models
Development and validation of a risk nomogram predicting pneumothorax requiring chest tube placement post-percutaneous CT-guided lung biopsy
Conference Presentation
2023
Clinical and Imaging Factors Associated with Growth of Subsolid Pulmonary Nodule on CT