Detection of Pneumothorax with Deep Learning Models: Learning From Radiologist Labels vs Natural Language Processing Model Generated Labels

2021 ◽  
Author(s):  
James Thomas Patrick Decourcy Hallinan ◽  
Mengling Feng ◽  
Dianwen Ng ◽  
Soon Yiew Sia ◽  
Vincent Tze Yang Tiong ◽  
...  
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Learning Models

Leveraging Natural Language Processing Applications Using Machine Learning

2020 ◽  
pp. 338-360
Author(s):  
Janjanam Prabhudas ◽  
C. H. Pradeep Reddy
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Text Summarization ◽  
Feature Representation ◽  
Learning Models ◽  
Primary Focus ◽  
And Performance

The enormous increase of information along with the computational abilities of machines created innovative applications in natural language processing by invoking machine learning models. This chapter will project the trends of natural language processing by employing machine learning and its models in the context of text summarization. This chapter is organized to make the researcher understand technical perspectives regarding feature representation and their models to consider before applying on language-oriented tasks. Further, the present chapter revises the details of primary models of deep learning, its applications, and performance in the context of language processing. The primary focus of this chapter is to illustrate the technical research findings and gaps of text summarization based on deep learning along with state-of-the-art deep learning models for TS.

scholarly journals Automatic Diagnosis of Spinal Disorders on Radiographic Images: Leveraging Existing Unstructured Datasets With Natural Language Processing

2021 ◽  
pp. 219256822110269
Author(s):  
Fabio Galbusera ◽  
Andrea Cina ◽  
Tito Bassani ◽  
Matteo Panico ◽  
Luca Maria Sconfienza
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Lumbar Spine ◽  
Natural Language ◽  
Language Processing ◽  
Large Set ◽  
Learning Models ◽  
Radiological Findings ◽  
Radiographic Images

Study Design: Retrospective study. Objectives: Huge amounts of images and medical reports are being generated in radiology departments. While these datasets can potentially be employed to train artificial intelligence tools to detect findings on radiological images, the unstructured nature of the reports limits the accessibility of information. In this study, we tested if natural language processing (NLP) can be useful to generate training data for deep learning models analyzing planar radiographs of the lumbar spine. Methods: NLP classifiers based on the Bidirectional Encoder Representations from Transformers (BERT) model able to extract structured information from radiological reports were developed and used to generate annotations for a large set of radiographic images of the lumbar spine (N = 10 287). Deep learning (ResNet-18) models aimed at detecting radiological findings directly from the images were then trained and tested on a set of 204 human-annotated images. Results: The NLP models had accuracies between 0.88 and 0.98 and specificities between 0.84 and 0.99; 7 out of 12 radiological findings had sensitivity >0.90. The ResNet-18 models showed performances dependent on the specific radiological findings with sensitivities and specificities between 0.53 and 0.93. Conclusions: NLP generates valuable data to train deep learning models able to detect radiological findings in spine images. Despite the noisy nature of reports and NLP predictions, this approach effectively mitigates the difficulties associated with the manual annotation of large quantities of data and opens the way to the era of big data for artificial intelligence in musculoskeletal radiology.

Comparison of deep learning models for natural language processing-based classification of non-English head CT reports

2020 ◽  
Vol 62 (10) ◽  
pp. 1247-1256 ◽  
Author(s):  
Yiftach Barash ◽  
Gennadiy Guralnik ◽  
Noam Tau ◽  
Shelly Soffer ◽  
Tal Levy ◽  
...  
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Learning Models ◽  
Head Ct

Adversarial Attacks on Deep-learning Models in Natural Language Processing

2020 ◽  
Vol 11 (3) ◽  
pp. 1-41 ◽  
Author(s):  
Wei Emma Zhang ◽  
Quan Z. Sheng ◽  
Ahoud Alhazmi ◽  
Chenliang Li
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Learning Models

scholarly journals Natural language inference by deep learning method

2022 ◽  
Vol 355 ◽  
pp. 03028
Author(s):  
Saihan Li ◽  
Zhijie Hu ◽  
Rong Cao
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Learning Method ◽  
Word Embeddings ◽  
Learning Models ◽  
The Relationship ◽  
Inference Task

Natural Language inference refers to the problem of determining the relationships between a premise and a hypothesis, it is an emerging area of natural language processing. The paper uses deep learning methods to complete natural language inference task. The dataset includes 3GPP dataset and SNLI dataset. Gensim library is used to get the word embeddings, there are 2 methods which are word2vec and doc2vec to map the sentence to array. 2 deep learning models DNNClassifier and Attention are implemented separately to classify the relationship between the proposals from the telecommunication area dataset. The highest accuracy of the experiment is 88% and we found that the quality of the dataset decided the upper bound of the accuracy.

scholarly journals UMLS-based data augmentation for natural language processing of clinical research literature

2020 ◽  
Author(s):  
Tian Kang ◽  
Adler Perotte ◽  
Youlan Tang ◽  
Casey Ta ◽  
Chunhua Weng
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Data Augmentation ◽  
Training Data ◽  
Entity Recognition ◽  
Classification Model ◽  
Learning Models ◽  
Sentence Classification

Abstract Objective The study sought to develop and evaluate a knowledge-based data augmentation method to improve the performance of deep learning models for biomedical natural language processing by overcoming training data scarcity. Materials and Methods We extended the easy data augmentation (EDA) method for biomedical named entity recognition (NER) by incorporating the Unified Medical Language System (UMLS) knowledge and called this method UMLS-EDA. We designed experiments to systematically evaluate the effect of UMLS-EDA on popular deep learning architectures for both NER and classification. We also compared UMLS-EDA to BERT. Results UMLS-EDA enables substantial improvement for NER tasks from the original long short-term memory conditional random fields (LSTM-CRF) model (micro-F1 score: +5%, + 17%, and +15%), helps the LSTM-CRF model (micro-F1 score: 0.66) outperform LSTM-CRF with transfer learning by BERT (0.63), and improves the performance of the state-of-the-art sentence classification model. The largest gain on micro-F1 score is 9%, from 0.75 to 0.84, better than classifiers with BERT pretraining (0.82). Conclusions This study presents a UMLS-based data augmentation method, UMLS-EDA. It is effective at improving deep learning models for both NER and sentence classification, and contributes original insights for designing new, superior deep learning approaches for low-resource biomedical domains.

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