5 papers
CheXTemporal: A Dataset for Temporally-Grounded Reasoning in Chest Radiography
Eva Prakash, Yunhe Gao, Chong Wang +10
Chest radiograph interpretation requires temporal reasoning over prior and current studies, yet most vision-language models are trained on static image-report pairs and lack explic…
Improving the Performance of Radiology Report De-identification with Large-Scale Training and Benchmarking Against Cloud Vendor Methods
Eva Prakash, Maayane Attias, Pierre Chambon +5
Objective: To enhance automated de-identification of radiology reports by scaling transformer-based models through extensive training datasets and benchmarking performance against…
Cost and Reward Infused Metric Elicitation
Chethan Bhateja, Joseph O'Brien, Afnaan Hashmi +1
In machine learning, metric elicitation refers to the selection of performance metrics that best reflect an individual's implicit preferences for a given application. Currently, me…
Evaluating and Improving the Effectiveness of Synthetic Chest X-Rays for Medical Image Analysis
Eva Prakash, Jeya Maria Jose Valanarasu, Zhihong Chen +9
Purpose: To explore best-practice approaches for generating synthetic chest X-ray images and augmenting medical imaging datasets to optimize the performance of deep learning models…
Auto-Generating Weak Labels for Real & Synthetic Data to Improve Label-Scarce Medical Image Segmentation
Tanvi Deshpande, Eva Prakash, Elsie Gyang Ross +3
The high cost of creating pixel-by-pixel gold-standard labels, limited expert availability, and presence of diverse tasks make it challenging to generate segmentation labels to tra…