Multi-Dimensional Evaluation Metrics for Chest X-Ray Reports

In the past few years, there has been abundant research in using machine learning to generate high quality radiology reports using the large MIMIC-CXR chest x-ray dataset. However, there has been little work focused on evaluating the quality of generated reports from a clinical perspective, where accuracy is the most important factor. Current evaluation metrics evaluate reports in one dimension. This work proposes the use of multiple dimensions (factual correctness, comprehensiveness, style, and overall quality) to better capture evaluation preferences of a clinical text generating model where preferences can differ based on the use case. This work also presents a dataset of radiologist rating annotations for generated and reference chest x-ray radiology reports. Lastly, it also creates an improved metric for the readability dimension by adding context awareness of frequent and acceptable medical terminology.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Rawat, Saumya

Advisor dc:contributor.advisor

• Szolovits, Peter

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/144486

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/144486