Weakly Supervised Contrastive Learning for Chest X-Ray Report Generation
An Yan, Zexue He, Xing Lü, Du Jiang, Eric Chang, Amilcare Gentili, Julian McAuley, Chun‐Nan Hsu · 2021
Radiology report generation aims at generating descriptive text from radiology images automatically, which may present an opportunity to improve radiology reporting and interpretation.A typical setting consists of training encoder-decoder models on image-report pairs with a cross entropy loss, which struggles to generate informative sentences for clinical diagnoses since normal findings dominate the datasets.To tackle this challenge and encourage more clinically-accurate text outputs, we propose a novel weakly supervised contrastive loss for medical report generation.Experimental results demonstrate that our method benefits from contrasting target reports with incorrect but semantically-close ones.It outperforms previous work on both clinical correctness and text generation metrics for two public benchmarks.