CheXPrune: sparse chest X-ray report generation model using multi-attention and one-shot global pruning.

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Abstract

Automatic radiological report generation (ARRG) smoothens the clinical workflow by speeding the report generation task. Recently, various deep neural networks (DNNs) have been used for report generation and have achieved promising results. Despite the impressive results, their deployment remains challenging because of their size and complexity. Researchers have proposed several pruning methods to reduce the size of DNNs. Inspired by the one-shot weight pruning methods, we present CheXPrune, a multi-attention based sparse radiology report generation method. It uses encoder-decoder based architecture equipped with a visual and semantic attention mechanism. The model is 70% pruned during the training to achieve 3.33 × compression without sacrificing its accuracy. The empirical results evaluated on the OpenI dataset using BLEU, ROUGE, and CIDEr metrics confirm the accuracy of the sparse model viz- a ` -viz the dense model.© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.