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Deep learning in generating radiology reports: A survey

Maram Mahmoud A. Monshi, Josiah Poon, Vera Chung
2020 Artificial Intelligence in Medicine  
Substantial progress has been made towards implementing automated radiology reporting models based on deep learning (DL). This is due to the introduction of large medical text/image datasets.  ...  This survey will be useful for researchers interested in DL, particularly those interested in applying DL to radiology reporting.  ...  Notably, CNN and RNN have quickly become popular choices for mining radiology images and text, respectively.  ... 
doi:10.1016/j.artmed.2020.101878 pmid:32425358 pmcid:PMC7227610 fatcat:ccy2g2rh2zavdjjvvjlv7poxau

Interleaved Text/Image Deep Mining on a Large-Scale Radiology Database for Automated Image Interpretation [article]

Hoo-Chang Shin, Le Lu, Lauren Kim, Ari Seff, Jianhua Yao, Ronald M. Summers
2015 arXiv   pre-print
We present an interleaved text/image deep learning system to extract and mine the semantic interactions of radiology images and reports from a national research hospital's Picture Archiving and Communication  ...  With natural language processing, we mine a collection of representative ~216K two-dimensional key images selected by clinicians for diagnostic reference, and match the images with their descriptions in  ...  We thank NVIDIA for the K40 GPU donation.  ... 
arXiv:1505.00670v1 fatcat:pwpfinxrh5hixl6rnjezaqci3e

Automatic Prediction of Recurrence of Major Cardiovascular Events: A Text Mining Study Using Chest X-Ray Reports

Ayoub Bagheri, T. Katrien J. Groenhof, Folkert W. Asselbergs, Saskia Haitjema, Michiel L. Bots, Wouter B. Veldhuis, Pim A. de Jong, Daniel L. Oberski, Aiping Liu
2021 Journal of Healthcare Engineering  
We propose a deep learning-based multimodal architecture for our text mining pipeline that integrates neural text representation with preprocessed clinical predictors for the prediction of recurrence of  ...  The aim of this project was to develop and evaluate a text mining pipeline in a multimodal learning architecture to demonstrate the value of medical text classification in chest radiograph reports for  ...  Figure 4 illustrates the proposed deep learning-based architecture for the text mining pipeline.  ... 
doi:10.1155/2021/6663884 pmid:34306597 pmcid:PMC8285182 fatcat:olo6eox56vc6rf5dagh2y7vigi

A self-attention based deep learning method for lesion attribute detection from CT reports [article]

Yifan Peng, Ke Yan, Veit Sandfort, Ronald M. Summers, Zhiyong Lu
2019 arXiv   pre-print
This paper outlines a novel deep learning method to automatically extract attributes of lesions of interest from the clinical text.  ...  While these lesion attributes are rich and useful in many downstream clinical applications, how to extract them from the radiology reports is less studied.  ...  Summers, “Interleaved text/image deep mining on a large-scale radiology database for automated image interpretation,” The Journal of Machine Learning Research, vol. 17, no. 1, pp. 3729–3759  ... 
arXiv:1904.13018v1 fatcat:nztvz56jgffjxaoxtgy2q3x5c4

NLP Algorithms Endowed for Automatic Extraction of Information from Unstructured Free-Text Reports of Radiology Monarchy

2020 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
Natural Language Processing (NLP) Algorithms are the key factors for automatic information extraction form the unstructured free-text radiology reports .To extract clinically important findings and recommendations  ...  learning-based approaches.  ...  reports. 8 Support Vector Machines (SVMs) and deep learning .  ... 
doi:10.35940/ijitee.l8009.1091220 fatcat:sjth33dnvjfnhn442figt75llq

Multimodal Learning for Cardiovascular Risk Prediction using EHR Data [article]

Ayoub Bagheri, T. Katrien J. Groenhof, Wouter B. Veldhuis, Pim A. de Jong, Folkert W. Asselbergs, Daniel L. Oberski
2020 arXiv   pre-print
and chest X-ray radiology reports.  ...  Various machine learning approaches have been developed to employ information in EHRs for risk prediction.  ...  Acknowledgments The authors would like to thank Erik-Jan van Kesteren for his comments.  ... 
arXiv:2008.11979v1 fatcat:4qgn4jtuxncihboeuca3wtxj7q

The Rise of Deep Learning in Radiology: An Overview of Recent Research

Deya Chatterjee
2019 International Journal for Research in Applied Science and Engineering Technology  
Moreover, deep learning can also be applied to radiology use cases other than image interpretation, such as patient scheduling or the processing of free-text radiology reports to improve healthcare surveillance  ...  Hence, in the field of radiology too, especially for image interpretation tasks, deep learning techniques are being increasingly used in recent times to optimize the medical workflow and to achieve better  ...  Moreover, free-text radiology reports are often sources of essential data, but they require sophisticated text-mining methods to be processed in an automated way [8] .  ... 
doi:10.22214/ijraset.2019.6397 fatcat:473bjftzdvhlpaqsatvzfn3bf4

Unsupervised Joint Mining of Deep Features and Image Labels for Large-scale Radiology Image Categorization and Scene Recognition [article]

Xiaosong Wang, Le Lu, Hoo-chang Shin, Lauren Kim, Mohammadhadi Bagheri, Isabella Nogues, Jianhua Yao, Ronald M. Summers
2017 arXiv   pre-print
We address this problem by presenting a looped deep pseudo-task optimization (LDPO) framework for joint mining of deep CNN features and image labels.  ...  Our proposed method is validated in tackling two important applications: 1) Large-scale medical image annotation has always been a prohibitively expensive and easily-biased task even for well-trained radiologists  ...  This looped optimization algorithm flow starts with deep CNN feature extraction and image encoding using domainspecifically (e.g., CNN trained on radiology images and text report-derived labels [49] )  ... 
arXiv:1701.06599v2 fatcat:rxdvtbhgyfevvm24z7g66eliby

ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases

Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, Ronald M. Summers
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
In this paper, we present a new chest X-ray database, namely "ChestX-ray8", which comprises 108,948 frontal-view X-ray images of 32,717 unique patients with the text-mined eight disease image labels (where  ...  each image can have multi-labels), from the associated radiological reports using natural language processing.  ...  We thank NVIDIA Corporation for the GPU donation.  ... 
doi:10.1109/cvpr.2017.369 dblp:conf/cvpr/WangPLLBS17 fatcat:7fk6qbqutzd7flnh5jwioiyhou

Multimodal Representation Learning via Maximization of Local Mutual Information [article]

Ruizhi Liao, Daniel Moyer, Miriam Cha, Keegan Quigley, Seth Berkowitz, Steven Horng, Polina Golland, William M. Wells
2021 arXiv   pre-print
Our experimental results in the downstream image classification tasks demonstrate the advantages of using local features for image-text representation learning.  ...  We propose and demonstrate a representation learning approach by maximizing the mutual information between local features of images and text.  ...  In the context of medical imaging, the images could be, for example, radiographs and the text could be radiology reports that capture radiologists' impressions of the images.  ... 
arXiv:2103.04537v4 fatcat:4itsxi2myzcg3hyptj52qt7g3m

EuSoMII Academy 2017

2018 Insights into Imaging  
Interpretation of medical images is much more difficult than Deep Learning/Machine learning experts have anticipated for a variety of reasons and radiologists will not be replaced for quite a long time  ...  radiological community for quite a while.  ...  Furthermore, free-text reports should be abandoned and be replaced by structured reports. The obtained parameters guide the clinician in choosing the most value-adding treatment for the patient.  ... 
doi:10.1007/s13244-018-0632-3 pmid:29797012 pmcid:PMC5986663 fatcat:pk5inp6i5vcolozh6r53mijqqq

Templates, Modules, and Common Data Elements: Building Blocks of Structured Reporting

Mansoor Fatehi
2019 Iranian Journal of Radiology  
Email: erik.ran-schaert@gmail.com Abstract Background: Radiology is at the forefront of the revolution in medical imaging, which is mainly based on the progress made in machine learning and deep learning  ...  The current trend of using electronic tools for the enhancement of the quality INVITED ABSTRACTS of reports reveals that structured reporting has undeniable advantages over free-text reporting.  ...  Outline: The first part of the talk provides an overall review of some machine learning models developed for solving medical imaging problems.  ... 
doi:10.5812/iranjradiol.99228 fatcat:c46aibgkknfehawr5kopxxfvve

Pushing the Limits of Radiology with Joint Modeling of Visual and Textual Information

Sonit Singh
2018 Proceedings of ACL 2018, Student Research Workshop  
To address this research gap, we aim to develop machine learning models that can reason jointly on medical images and clinical text for advanced search, retrieval, annotation and description of medical  ...  Researchers have studied several interesting tasks, including generating text descriptions from images and videos and language embedding of images.  ...  Also thankful to Google for providing travel grant to attend the conference.  ... 
doi:10.18653/v1/p18-3005 dblp:conf/acl/Singh18 fatcat:moowp3tzoja6th62shsrufsruy

Text mining brain imaging reports

Beatrice Alex, Claire Grover, Richard Tobin, Cathie Sudlow, Grant Mair, William Whiteley
2019 Journal of Biomedical Semantics  
We describe a text mining system for classifying radiologists' reports of CT and MRI brain scans, assigning labels indicating occurrence and type of stroke, as well as other observations.  ...  Our system, the Edinburgh Information Extraction for Radiology reports (EdIE-R) system, which we describe here, was developed and tested on a collection of radiology reports.The work reported in this paper  ...  Availability of data and materials The annotated ESS corpus that we have created as part of this project has much potential value as a resource for developing text mining algorithms.  ... 
doi:10.1186/s13326-019-0211-7 pmid:31711539 pmcid:PMC6849161 fatcat:j3gz2wbswrcp3nlvq6dnfpdwni

A Natural Language Processing Pipeline of Chinese Free-text Radiology Reports for Liver Cancer Diagnosis

Honglei Liu, Yan Xu, Zhiqiang Zhang, Ni Wang, Yanqun Huang, Yanjun Hu, Zhenghan Yang, Rui Jiang, Hui Chen
2020 IEEE Access  
CONCLUSIONS This study described an NLP pipeline of Chinese free-text radiology reports for liver cancer diagnosis.  ...  In the consideration of the characteristics of radiology reports, we annotated five entity types and designed deep learningbased BiLSTM-CRF model for the NER task.  ... 
doi:10.1109/access.2020.3020138 fatcat:jgfachrsqfgwxggkbuug4r27pe
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