scholarly journals A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions

2020 ◽  
Vol 6 (12) ◽  
pp. 131
Author(s):  
Stefanus Tao Hwa Kieu ◽  
Abdullah Bade ◽  
Mohd Hanafi Ahmad Hijazi ◽  
Hoshang Kolivand
Keyword(s):  
Deep Learning ◽  
Lung Disease ◽  
Recent Work ◽  
Lung Diseases ◽  
Data Augmentation ◽  
State Of The Art ◽  
Medical Images ◽  
Disease Detection ◽  
Future Directions ◽  
Survey Paper

The recent developments of deep learning support the identification and classification of lung diseases in medical images. Hence, numerous work on the detection of lung disease using deep learning can be found in the literature. This paper presents a survey of deep learning for lung disease detection in medical images. There has only been one survey paper published in the last five years regarding deep learning directed at lung diseases detection. However, their survey is lacking in the presentation of taxonomy and analysis of the trend of recent work. The objectives of this paper are to present a taxonomy of the state-of-the-art deep learning based lung disease detection systems, visualise the trends of recent work on the domain and identify the remaining issues and potential future directions in this domain. Ninety-eight articles published from 2016 to 2020 were considered in this survey. The taxonomy consists of seven attributes that are common in the surveyed articles: image types, features, data augmentation, types of deep learning algorithms, transfer learning, the ensemble of classifiers and types of lung diseases. The presented taxonomy could be used by other researchers to plan their research contributions and activities. The potential future direction suggested could further improve the efficiency and increase the number of deep learning aided lung disease detection applications.

Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction

2020 ◽  
Author(s):  
Dean Sumner ◽  
Jiazhen He ◽  
Amol Thakkar ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Learning Models ◽  
Underlying Network

<p>SMILES randomization, a form of data augmentation, has previously been shown to increase the performance of deep learning models compared to non-augmented baselines. Here, we propose a novel data augmentation method we call “Levenshtein augmentation” which considers local SMILES sub-sequence similarity between reactants and their respective products when creating training pairs. The performance of Levenshtein augmentation was tested using two state of the art models - transformer and sequence-to-sequence based recurrent neural networks with attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and conventionally SMILES randomization augmented data when used for training of baseline models. Furthermore, Levenshtein augmentation seemingly results in what we define as <i>attentional gain </i>– an enhancement in the pattern recognition capabilities of the underlying network to molecular motifs.</p>

scholarly journals A Survey on Bias and Fairness in Machine Learning

2021 ◽  
Vol 54 (6) ◽  
pp. 1-35
Author(s):  
Ninareh Mehrabi ◽  
Fred Morstatter ◽  
Nripsuta Saxena ◽  
Kristina Lerman ◽  
Aram Galstyan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real World ◽  
State Of The Art ◽  
Future Directions ◽  
Discriminatory Behavior ◽  
Real World Applications ◽  
Near Future ◽  
Different Sources

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

scholarly journals One for All: Neural Joint Modeling of Entities and Events

2019 ◽  
Vol 33 ◽  
pp. 6851-6858 ◽  
Author(s):  
Trung Minh Nguyen ◽  
Thien Huu Nguyen
Keyword(s):  
Deep Learning ◽  
Recent Work ◽  
State Of The Art ◽  
Contextual Information ◽  
Joint Modeling ◽  
Event Extraction ◽  
Event Trigger ◽  
The Individual ◽  
Novel Model ◽  
Argument Roles

The previous work for event extraction has mainly focused on the predictions for event triggers and argument roles, treating entity mentions as being provided by human annotators. This is unrealistic as entity mentions are usually predicted by some existing toolkits whose errors might be propagated to the event trigger and argument role recognition. Few of the recent work has addressed this problem by jointly predicting entity mentions, event triggers and arguments. However, such work is limited to using discrete engineering features to represent contextual information for the individual tasks and their interactions. In this work, we propose a novel model to jointly perform predictions for entity mentions, event triggers and arguments based on the shared hidden representations from deep learning. The experiments demonstrate the benefits of the proposed method, leading to the state-of-the-art performance for event extraction.

scholarly journals Building Trust in Deep Learning System towards Automated Disease Detection

2019 ◽  
Vol 33 ◽  
pp. 9516-9521 ◽  
Author(s):  
Zhan Wei Lim ◽  
Mong Li Lee ◽  
Wynne Hsu ◽  
Tien Yin Wong
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Medical Images ◽  
Real Life ◽  
High Accuracy ◽  
Learning Systems ◽  
Learning System ◽  
Disease Detection ◽  
Building Trust ◽  
Uncertainty Estimates

Though deep learning systems have achieved high accuracy in detecting diseases from medical images, few such systems have been deployed in highly automated disease screening settings due to lack of trust in how well these systems can generalize to out-of-datasets. We propose to use uncertainty estimates of the deep learning system’s prediction to know when to accept or to disregard its prediction. We evaluate the effectiveness of using such estimates in a real-life application for the screening of diabetic retinopathy. We also generate visual explanation of the deep learning system to convey the pixels in the image that influences its decision. Together, these reveal the deep learning system’s competency and limits to the human, and in turn the human can know when to trust the deep learning system.

Deep Learning for Lung Disease Detection from Chest X-Rays Images

2021 ◽  
pp. 239-254
Author(s):  
Ebenezer Jangam ◽  
Chandra Sekhara Rao Annavarapu ◽  
Mourad Elloumi
Keyword(s):  
Deep Learning ◽  
Lung Disease ◽  
Disease Detection ◽  
X Rays

scholarly journals Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions

2017 ◽  
Vol 30 (4) ◽  
pp. 449-459 ◽  
Author(s):  
Zeynettin Akkus ◽  
Alfiia Galimzianova ◽  
Assaf Hoogi ◽  
Daniel L. Rubin ◽  
Bradley J. Erickson
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Brain Mri ◽  
Mri Segmentation ◽  
Future Directions

scholarly journals Deep learning in interstitial lung disease—how long until daily practice

2020 ◽  
Vol 30 (11) ◽  
pp. 6285-6292
Author(s):  
Ana Adriana Trusculescu ◽  
Diana Manolescu ◽  
Emanuela Tudorache ◽  
Cristian Oancea
Keyword(s):  
Deep Learning ◽  
Idiopathic Pulmonary Fibrosis ◽  
Pulmonary Fibrosis ◽  
Early Diagnosis ◽  
Lung Disease ◽  
Lung Diseases ◽  
Daily Practice ◽  
Interstitial Lung Diseases ◽  
Learning Approaches ◽  
High Resolution Computed Tomography

Abstract Interstitial lung diseases are a diverse group of disorders that involve inflammation and fibrosis of interstitium, with clinical, radiological, and pathological overlapping features. These are an important cause of morbidity and mortality among lung diseases. This review describes computer-aided diagnosis systems centered on deep learning approaches that improve the diagnostic of interstitial lung diseases. We highlighted the challenges and the implementation of important daily practice, especially in the early diagnosis of idiopathic pulmonary fibrosis (IPF). Developing a convolutional neuronal network (CNN) that could be deployed on any computer station and be accessible to non-academic centers is the next frontier that needs to be crossed. In the future, early diagnosis of IPF should be possible. CNN might not only spare the human resources but also will reduce the costs spent on all the social and healthcare aspects of this deadly disease. Key Points • Deep learning algorithms are used in pattern recognition of different interstitial lung diseases. • High-resolution computed tomography plays a central role in the diagnosis and in the management of all interstitial lung diseases, especially fibrotic lung disease. • Developing an accessible algorithm that could be deployed on any computer station and be used in non-academic centers is the next frontier in the early diagnosis of idiopathic pulmonary fibrosis.

scholarly journals Fast Deep Learning Computer-Aided Diagnosis against the Novel COVID-19 pandemic from Digital Chest X-ray Images

2020 ◽  
Author(s):  
Mugahed A. Al-antari ◽  
Cam-Hao Hua ◽  
Sungyoung Lee
Keyword(s):  
Deep Learning ◽  
Lung Disease ◽  
Lung Diseases ◽  
Diagnostic Performance ◽  
The Novel ◽  
X Ray ◽  
Cad System ◽  
Computer Aided ◽  
Chest X Ray ◽  
Aided Diagnosis

Abstract Background and Objective: The novel coronavirus 2019 (COVID-19) is a harmful lung disease that rapidly attacks people worldwide. At the end of 2019, COVID-19 was discovered as mysterious lung disease in Wuhan, Hubei province of China. World health organization (WHO) declared the coronavirus outbreak a pandemic in the second week of March 2020. Simultaneous deep learning detection and classification of COVID-19 from the entire digital X-ray images is the key to efficiently assist patients and physicians for a fast and accurate diagnosis.Methods: In this paper, a deep learning computer-aided diagnosis (CAD) based on the YOLO predictor is proposed to simultaneously detect and diagnose COVID-19 among the other eight lung diseases: Atelectasis, Infiltration, Pneumothorax, Mass, Effusion, Pneumonia, Cardiomegaly, and Nodule. The proposed CAD system is assessed via five-fold tests for multi-class prediction problem using two different databases of chest X-ray images: COVID-19 and ChestX-ray8. The proposed CAD system is trained using an annotated training set of 50,490 chest X-ray images.Results: The suspicious regions of COVID-19 from the entire X-ray images are simultaneously detected and classified end-to-end via the proposed CAD predictor achieving overall detection and classification accuracies of 96.31% and 97.40%, respectively. The most testing images of COVID-19 and other lunge diseases are correctly predicted achieving intersection over union (IoU) with their GTs greater than 90%. Applying deep learning regularizers of data balancing and augmentation improve the diagnostic performance by 6.64% and 12.17% in terms of overall accuracy and F1-score, respectively. Meanwhile, the proposed CAD system presents its feasibility to diagnose the individual chest X-ray image within 0.009 second. Thus, the presented CAD system could predict 108 frames/second (FPS) at the real-time of prediction.Conclusion: The proposed deep learning CAD system shows its capability and reliability to achieve promising COVID-19 diagnostic performance among all other lung diseases. The proposed deep learning model seems reliable to assist health care systems, patients, and physicians in their practical validations.

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