Encoder-decoder models for chest X-ray report generation perform no better than unconditioned baselines

High quality radiology reporting of chest X-ray images is of core importance for high-quality patient diagnosis and care. Automatically generated reports can assist radiologists by reducing their workload and even may prevent errors. Machine Learning (ML) models for this task take an X-ray image as...

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Vydané v:PloS one Ročník 16; číslo 11; s. e0259639
Hlavní autori: Babar, Zaheer, van Laarhoven, Twan, Marchiori, Elena
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Public Library of Science 29.11.2021
Public Library of Science (PLoS)
Predmet:
Algorithms
Automation
Chest
Coders
Computer and Information Sciences
Diagnostic systems
Encoders-Decoders
Health aspects
Image quality
Information science
Keywords
Learning algorithms
Machine learning
Medical diagnosis
Medical imaging
Medicine and Health Sciences
Model accuracy
Neural networks
People and Places
Radiology
Research and Analysis Methods
Semantics
Social Sciences
Tomography, X-Ray Computed
X-rays
Netherlands
ISSN:1932-6203, 1932-6203
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Shrnutí:High quality radiology reporting of chest X-ray images is of core importance for high-quality patient diagnosis and care. Automatically generated reports can assist radiologists by reducing their workload and even may prevent errors. Machine Learning (ML) models for this task take an X-ray image as input and output a sequence of words. In this work, we show that ML models for this task based on the popular encoder-decoder approach, like ‘Show, Attend and Tell’ (SA&T) have similar or worse performance than models that do not use the input image, called unconditioned baseline. An unconditioned model achieved diagnostic accuracy of 0.91 on the IU chest X-ray dataset, and significantly outperformed SA&T (0.877) and other popular ML models ( p -value < 0.001). This unconditioned model also outperformed SA&T and similar ML methods on the BLEU-4 and METEOR metrics. Also, an unconditioned version of SA&T obtained by permuting the reports generated from images of the test set, achieved diagnostic accuracy of 0.862, comparable to that of SA&T ( p -value ≥ 0.05).
Bibliografia:ObjectType-Article-1
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Competing Interests: NO authors have competing interests.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0259639