Sharpness matters: Higher image resolution improves generalization and explainability in chest X-ray classification.

Aditya V Kulkarni¹, John R Zech², Paul H Yi³

¹Department of Radiology, St. Jude Children's Research Hospital, 262 Danny Thomas Pl, Memphis, TN, 38105, USA.

²Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA.

³Department of Radiology, St. Jude Children's Research Hospital, 262 Danny Thomas Pl, Memphis, TN, 38105, USA. paul.yi@stjude.org.

PubMed PMC DOI PDF

Abstract

Deep learning (DL) chest radiograph (CXR) models are often trained on downsampled images to reduce computational overhead, despite clinical workflows operating at high resolution. Previous studies have investigated the impact of input resolution on CXR classification accuracy, yet two fundamental pillars of safe and trustworthy AI, explainability and generalizability, remain underexplored. In this…

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