Learning to Summarize Radiology Findings

Zhang, Yuhao; Ding, Daisy Yi; Qian, Tianpei; Manning, Christopher D.; Langlotz, Curtis P.

doi:10.18653/v1/w18-5623

Cited by 86 publications

(87 citation statements)

References 21 publications

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“…Recently, deep learning methods have also been used for automated generation of radiology reports, typically incorporating long-short-term-memory (LSTM) network models to generate the textual paragraphs [314,315,316,317], and also to identify findings in radiology reports [318,319,320].…”

Section: Content-based Image Retrievalmentioning

confidence: 99%

An overview of deep learning in medical imaging focusing on MRI

Lundervold

2019

Zeitschrift für Medizinische Physik

1,430

883

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What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI.Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of machine learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging. Artificial neural networksArtificial neural networks (ANNs) is one of the most famous machine learning models, introduced already in the 1950s, and actively studied since [41, Chapter 1.2]. 11 Roughly, a neural network consists of a number of connected computational units, called neurons, arranged in layers. There's an input layer where data enters the network, followed by one or more hidden layers transforming the data as it flows through, before ending at an output layer that produces the neural network's predictions. The network is trained to output useful predictions by identifying patterns in a set of labeled training data, fed through the network while the outputs are compared with the actual labels by an objective function. During training the network's parametersthe strength of each neuron-is tuned until the patterns identified by the network result in good 10 https://www.deeplearningbook.org/ 11 The loose connection between artificial neural networks and neural networks in the brain is often mentioned, but quite over-blown considering the complexity of biological neural networks. However, there is some interesting recent work connecting neuroscience and artificial neural networks, indicating an increase in the cross-fertilization between the two fields [43,44,45].3 predictions for the training data. Once the patterns are learned, the network can be used to make predictions on new, unseen data, i.e. generalize to new data.It h...

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Section: Content-based Image Retrievalmentioning

confidence: 99%

An overview of deep learning in medical imaging focusing on MRI

Lundervold

2019

Zeitschrift für Medizinische Physik

1,430

883

View full text Add to dashboard Cite

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“…The baseline model is a pointer-generator network (See et al, 2017) with both copy and coverage mechanisms, and is trained with a coverage loss. We adopt the implementation of Zhang et al (2018). The vocabulary size is about 50,000, with uncased word embeddings pretrained from the PubMed RCT 200k training set and the abstracts from the PubMed dataset of long documents (Cohan et al, 2018).…”

Section: Methodsmentioning

confidence: 99%

“…Several medical domain natural language generation tasks have been studied using machine learning models, including generating radiology reports from images (Jing et al, 2018;Vaswani et al, 2017) and summarizing clinical reports (Zhang et al, 2018;Pivovarov and Elhadad, 2015) or research literature (Cohan et al, 2018). Recently, Gulden et al (2019) studied extractive summarization on RCT descriptions.…”

Section: Medical Natural Language Generationmentioning

confidence: 99%

Towards Understanding of Medical Randomized Controlled Trials by Conclusion Generation

Shieh

Chuang

et al. 2019

Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)

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Randomized controlled trials (RCTs) represent the paramount evidence of clinical medicine. Using machines to interpret the massive amount of RCTs has the potential of aiding clinical decision-making. We propose a RCT conclusion generation task from the PubMed 200k RCT sentence classification dataset to examine the effectiveness of sequence-to-sequence models on understanding RCTs. We first build a pointergenerator baseline model for conclusion generation. Then we fine-tune the state-of-the-art GPT-2 language model, which is pre-trained with general domain data, for this new medical domain task. Both automatic and human evaluation show that our GPT-2 fine-tuned models achieve improved quality and correctness in the generated conclusions compared to the baseline pointer-generator model. Further inspection points out the limitations of this current approach and future directions to explore * .

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“…Here, we focus on summarization of clinical notes where content accuracy and completeness are more critical. The most relevant work to ours is by Zhang et al [19] where an additional section from the radiology report (background) is used to improve summarization. Extensive automated and human evaluation and analyses demonstrate the benefits of our proposed model in comparison with existing work.…”

Section: Related Workmentioning

confidence: 99%

Ontology-Aware Clinical Abstractive Summarization

MacAvaney

Sotudeh

Cohan

et al. 2019

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

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Automatically generating accurate summaries from clinical reports could save a clinician's time, improve summary coverage, and reduce errors. We propose a sequence-to-sequence abstractive summarization model augmented with domain-specific ontological information to enhance content selection and summary generation. We apply our method to a dataset of radiology reports and show that it significantly outperforms the current state-of-the-art on this task in terms of rouge scores. Extensive human evaluation conducted by a radiologist further indicates that this approach yields summaries that are less likely to omit important details, without sacrificing readability or accuracy.

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Learning to Summarize Radiology Findings

Cited by 86 publications

References 21 publications

An overview of deep learning in medical imaging focusing on MRI

An overview of deep learning in medical imaging focusing on MRI

Towards Understanding of Medical Randomized Controlled Trials by Conclusion Generation

Ontology-Aware Clinical Abstractive Summarization

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