Deep Learning for Chest Radiology: A Review

Feng, Yeli; Teh, Hui Seong; Cai, Yiyu

doi:10.1007/s40134-019-0333-9

Cited by 21 publications

(14 citation statements)

References 20 publications

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“…Recent studies have demonstrated that the systematic analysis of genomic data with AI/ML technology can favor precision medicine for the benefit of patients 5 , 6 . Although the most widely used AI/ML technology in respiratory diseases is chest imaging, especially for the screening and diagnosis of lung nodules, the application of AI/ML tools in chronic airway diseases is attracting increasing attention 7 , 8 .…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease

Feng¹,

Wang²,

Zeng³

et al. 2021

Int. J. Med. Sci.

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Chronic airway diseases are characterized by airway inflammation, obstruction, and remodeling and show high prevalence, especially in developing countries. Among them, asthma and chronic obstructive pulmonary disease (COPD) show the highest morbidity and socioeconomic burden worldwide. Although there are extensive guidelines for the prevention, early diagnosis, and rational treatment of these lifelong diseases, their value in precision medicine is very limited. Artificial intelligence (AI) and machine learning (ML) techniques have emerged as effective methods for mining and integrating large-scale, heterogeneous medical data for clinical practice, and several AI and ML methods have recently been applied to asthma and COPD. However, very few methods have significantly contributed to clinical practice. Here, we review four aspects of AI and ML implementation in asthma and COPD to summarize existing knowledge and indicate future steps required for the safe and effective application of AI and ML tools by clinicians.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease

Feng¹,

Wang²,

Zeng³

et al. 2021

Int. J. Med. Sci.

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“…Hochreiter and Schmidhuber [ 46 ] showed a variation of a recurrent neural network named Long Short Term Memory network (LSTM) that with a special hidden unit acting like a memory cell plus a gradient-based back-propagation technique makes it possible to selectively retain relevant information from previous step, while the input sequence is being parsed element by element [ 47 ]. Afterwards, Bi-directional Long Short-Term Memory (BiLSTM) is an extension of traditional LSTM that can improve model performance on sequence classification problems [ 48 ].…”

Section: Automatic Classification Methodologymentioning

confidence: 99%

COVID-19 detection in radiological text reports integrating entity recognition

López-Úbeda

Díaz–Galiano

Martín-Noguerol³

et al. 2020

Computers in Biology and Medicine

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COVID-19 diagnosis is usually based on PCR test using radiological images, mainly chest Computed Tomography (CT) for the assessment of lung involvement by COVID-19. However, textual radiological reports also contain relevant information for determining the likelihood of presenting radiological signs of COVID-19 involving lungs. The development of COVID-19 automatic detection systems based on Natural Language Processing (NLP) techniques could provide a great help in supporting clinicians and detecting COVID-19 related disorders within radiological reports. In this paper we propose a text classification system based on the integration of different information sources. The system can be used to automatically predict whether or not a patient has radiological findings consistent with COVID-19 on the basis of radiological reports of chest CT. To carry out our experiments we use 295 radiological reports from chest CT studies provided by the ‘‘HT médica" clinic. All of them are radiological requests with suspicions of chest involvement by COVID-19. In order to train our text classification system we apply Machine Learning approaches and Named Entity Recognition. The system takes two sources of information as input: the text of the radiological report and COVID-19 related disorders extracted from SNOMED-CT. The best system is trained using SVM and the baseline results achieve 85% accuracy predicting lung involvement by COVID-19, which already offers competitive values that are difficult to overcome. Moreover, we apply mutual information in order to integrate the best quality information extracted from SNOMED-CT. In this way, we achieve around 90% accuracy improving the baseline results by 5 points.

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“…The attention-guided networks are trained based on attention mechanism ( Feng, Seong Teh & Cai, 2019 ). The localized attention improves the performance of the network by making a future reference point, hence the models focus more on a specific region, where the attention is pointing.…”

Section: Related Workmentioning

confidence: 99%

“…( Guan & Huang (2018) also use a technique with an attention-guided mechanism to classify chest X-ray images for thorax disease detection. The attention-guided networks are not free from certain disadvantages and this type of network can easily be fooled ( Feng, Seong Teh & Cai, 2019 ). There are several other drawbacks, e.g.…”

Section: Related Workmentioning

confidence: 99%

Gender and age detection assist convolutional neural networks in classification of thorax diseases

Ali

2021

PeerJ Computer Science

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Conventionally, convolutional neural networks (CNNs) have been used to identify and detect thorax diseases on chest x-ray images. To identify thorax diseases, CNNs typically learn two types of information: disease-specific features and generic anatomical features. CNNs focus on disease-specific features while ignoring the rest of the anatomical features during their operation. There is no evidence that generic anatomical features improve or worsen the performance of convolutional neural networks for thorax disease classification in the current research. As a consequence, the relevance of general anatomical features in boosting the performance of CNNs for thorax disease classification is investigated in this study. We employ a dual-stream CNN model to learn anatomical features before training the model for thorax disease classification. The dual-stream technique is used to compel the model to learn structural information because initial layers of CNNs often learn features of edges and boundaries. As a result, a dual-stream model with minimal layers learns structural and anatomical features as a priority. To make the technique more comprehensive, we first train the model to identify gender and age and then classify thorax diseases using the information acquired. Only when the model learns the anatomical features can it detect gender and age. We also use Non-negative Matrix Factorization (NMF) and Contrast Limited Adaptive Histogram Equalization (CLAHE) to pre-process the training data, which suppresses disease-related information while amplifying general anatomical features, allowing the model to acquire anatomical features considerably faster. Finally, the model that was earlier trained for gender and age detection is retrained for thorax disease classification using original data. The proposed technique increases the performance of convolutional neural networks for thorax disease classification, as per experiments on the Chest X-ray14 dataset. We can also see the significant parts of the image that contribute more for gender, age, and a certain thorax disease by visualizing the features. The proposed study achieves two goals: first, it produces novel gender and age identification results on chest X-ray images that may be used in biometrics, forensics, and anthropology, and second, it highlights the importance of general anatomical features in thorax disease classification. In comparison to state-of-the-art results, the proposed work also produces competitive results.

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Deep Learning for Chest Radiology: A Review

Cited by 21 publications

References 20 publications

Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease

Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease

COVID-19 detection in radiological text reports integrating entity recognition

Gender and age detection assist convolutional neural networks in classification of thorax diseases

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