55 citations · 108 across the 8 of their papers we have counts for
5 papers · 1 filter
LEAVS: An LLM-based Labeler for Abdominal CT Supervision
Ricardo Bigolin Lanfredi, Yan Zhuang, Mark Finkelstein +7
Extracting structured labels from radiology reports has been employed to create vision models to simultaneously detect several types of abnormalities. However, existing works focus…
Enhancing chest X-ray datasets with privacy-preserving large language models and multi-type annotations: a data-driven approach for improved classification
Ricardo Bigolin Lanfredi, Pritam Mukherjee, Ronald Summers
In chest X-ray (CXR) image analysis, rule-based systems are usually employed to extract labels from reports for dataset releases. However, there is still room for improvement in la…
REFLACX, a dataset of reports and eye-tracking data for localization of abnormalities in chest x-rays
Ricardo Bigolin Lanfredi, Mingyuan Zhang, William F. Auffermann +6
Deep learning has shown recent success in classifying anomalies in chest x-rays, but datasets are still small compared to natural image datasets. Supervision of abnormality localiz…
Interpretation of Disease Evidence for Medical Images Using Adversarial Deformation Fields
Ricardo Bigolin Lanfredi, Joyce D. Schroeder, Clement Vachet +1
The high complexity of deep learning models is associated with the difficulty of explaining what evidence they recognize as correlating with specific disease labels. This informati…
Adversarial regression training for visualizing the progression of chronic obstructive pulmonary disease with chest x-rays
Ricardo Bigolin Lanfredi, Joyce D. Schroeder, Clement Vachet +1
Knowledge of what spatial elements of medical images deep learning methods use as evidence is important for model interpretability, trustiness, and validation. There is a lack of s…