Xing Lu


2021

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Weakly Supervised Contrastive Learning for Chest X-Ray Report Generation
An Yan | Zexue He | Xing Lu | Jiang Du | Eric Chang | Amilcare Gentili | Julian McAuley | Chun-Nan Hsu
Findings of the Association for Computational Linguistics: EMNLP 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.