4.7 Article

Deep learning for understanding multilabel imbalanced Chest X-ray datasets

Related references

Note: Only part of the references are listed.
Article Engineering, Biomedical

Lesion attentive thoracic disease diagnosis with large decision margin loss

Tao Zhang et al.

Summary: The study proposes an improved deep model with two novel components, the lesion attentive network and the large decision margin loss, aiming to address the obstacles in chest X-ray image analysis. The proposed approach achieves state-of-the-art thorax disease diagnosis performances on the validation and test set, demonstrating its superiority in accuracy and efficiency.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2022)

Article Computer Science, Artificial Intelligence

GraphXCOVID: Explainable deep graph diffusion pseudo-Labelling for identifying COVID-19 on chest X-rays

Angelica Aviles-Rivero et al.

Summary: Semi-supervised learning shows promise in COVID-19 diagnosis, outperforming supervised models with just a small fraction of labeled examples. Researchers introduce a graph-based deep semi-supervised framework that optimizes the relation between labeled and unlabeled data, and provide visualizations to assist radiologists in diagnosis.

PATTERN RECOGNITION (2022)

Article Computer Science, Artificial Intelligence

A study on specific learning algorithms pertaining to classify lung cancer disease

Malavika Saminathan et al.

Summary: Lung cancer is a worldwide dangerous disease. By utilizing a combination of deep learning and machine learning methods, the accuracy of lung cancer diagnosis can be improved, leading to better early detection and treatment outcomes.

EXPERT SYSTEMS (2022)

Article Computer Science, Artificial Intelligence

EfficientUNet: Modified encoder-decoder architecture for the lung segmentation in chest x-ray images

Tarun Agrawal et al.

Summary: Chest x-rays are widely used for early detection of pulmonary diseases, but the increasing workload and shortage of radiologists have prompted researchers to explore automated diagnosis. Segmentation is critical for improving automatic diagnosis, but identifying lung boundaries is difficult due to irregular shape and size and overlapping lung regions. UNet is the most popular segmentation architecture, but there is room for improvement. Most proposed models for lung segmentation in chest x-rays are trained and tested on the same dataset, resulting in failure when tested on another dataset. This study aims to develop an efficient architecture for accurate lung segmentation using pre-trained encoders, residual learning, and batch normalization.

EXPERT SYSTEMS (2022)

Article Computer Science, Artificial Intelligence

Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence

Andreas Holzinger et al.

Summary: Medical artificial intelligence systems have achieved significant success and are crucial for improving human health. In order to enhance performance, addressing uncertainty and errors while explaining the result process is essential. Information fusion can help develop more robust and explainable machine learning models.

INFORMATION FUSION (2022)

Article Construction & Building Technology

Internet of health things driven deep learning-based system for non-invasive patient discomfort detection using time frame rules and pairwise keypoints distance feature

Imran Ahmed et al.

Summary: This article introduces a deep learning-based system for non-invasive patient discomfort detection by utilizing the Internet of Health Things (IoHT) concept. The system uses an RGB camera device to identify discomfort in a patient's body and continuously monitors the patient's comfort and discomfort levels.

SUSTAINABLE CITIES AND SOCIETY (2022)

Article Computer Science, Information Systems

Anatomy-XNet: An Anatomy Aware Convolutional Neural Network for Thoracic Disease Classification in Chest X-Rays

Uday Kamal et al.

Summary: Thoracic disease detection from chest radiographs using deep learning methods has shown great potential in recent years. This paper proposes a novel classification network, Anatomy-XNet, that integrates anatomical knowledge to improve disease classification. Experimental results on large-scale datasets demonstrate the effectiveness and generalizability of the proposed framework.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2022)

Article Computer Science, Artificial Intelligence

COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization

Tahmina Zebin et al.

Summary: The study utilized transfer learning to classify and detect COVID-19 chest X-ray images with an accuracy of up to 96.8%. Additionally, a generative adversarial network was used to generate and enhance the COVID-19 class, while a gradient class activation mapping technique was implemented for result interpretation.

APPLIED INTELLIGENCE (2021)

Article Computer Science, Artificial Intelligence

COVIDScreen: explainable deep learning framework for differential diagnosis of COVID-19 using chest X-rays

Rajeev Kumar Singh et al.

Summary: The article introduces a novel deep learning solution using chest X-rays for rapid triaging of COVID-19 patients, addressing the issue of scarce testing resources for COVID-19 detection.

NEURAL COMPUTING & APPLICATIONS (2021)

Article Computer Science, Artificial Intelligence

Artificial intelligence and healthcare: Forecasting of medical bookings through multi-source time-series fusion

Francesco Piccialli et al.

Summary: This paper presents a multi-source time series fusion and forecasting framework based on Deep Learning, which provides reliable medical predictions by preserving temporal patterns through a feature compression stage. The system is capable of predicting the number of bookings for a specific medical examination for a 7-day horizon period.

INFORMATION FUSION (2021)

Article Mathematical & Computational Biology

Deep Ensemble Model for Classification of Novel Coronavirus in Chest X-Ray Images

Fareed Ahmad et al.

Summary: The novel coronavirus poses a serious threat to human health, with chest X-rays being a crucial method for early diagnosis but difficult to interpret. Utilizing deep learning models for automatic classification can enhance assessment efficiency.

COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE (2021)

Article Computer Science, Artificial Intelligence

A survey on deep learning in medicine: Why, how and when?

Francesco Piccialli et al.

Summary: New technologies are revolutionizing medicine, with a key role played by data. Artificial intelligence, especially Deep Learning, is well-suited to handle the exponential growth of health-related information in the field of medicine, helping to build optimal neural networks for clinical problems as the amount of training data increases.

INFORMATION FUSION (2021)

Article Chemistry, Analytical

Impact of Lung Segmentation on the Diagnosis and Explanation of COVID-19 in Chest X-ray Images

Lucas O. Teixeira et al.

Summary: The study demonstrated the impact of lung segmentation in COVID-19 identification using CXR images, achieving good Jaccard distance and Dice coefficient for segmentation. It investigated the generalization of COVID-19 from images created from different sources, finding a strong bias introduced by underlying factors from different sources even after segmentation.

SENSORS (2021)

Article Computer Science, Information Systems

Deep Learning Methods in Internet of Medical Things for Valvular Heart Disease Screening System

Yu-Sheng Su et al.

Summary: The heart plays a vital role in circulating blood and delivering oxygen and nutrients to organs in the body. As the body ages, cardiac function can deteriorate, leading to cardiovascular diseases. Integrating the Internet of Medical Things into heart disease screening systems allows for early detection through self-examinations.

IEEE INTERNET OF THINGS JOURNAL (2021)

Article Computer Science, Artificial Intelligence

AI-driven deep CNN approach for multi-label pathology classification using chest X-Rays

Saleh Albahli et al.

Summary: Artificial intelligence plays a significant role in detecting and diagnosing a wide range of chest-related diseases through image analysis and feature extraction. This research proposes using synthetic data augmentation in three deep Convolutional Neural Networks architectures to detect 14 chest-related diseases with competitive ROC-AUC scores. The models DenseNet121, InceptionResNetV2, and ResNet152V2 were trained for multi-class classification, achieving better accuracy in classifying chest-related diseases.

PEERJ COMPUTER SCIENCE (2021)

Article Computer Science, Theory & Methods

Ensembles of Convolutional Neural Network models for pediatric pneumonia diagnosis

Helena Liz et al.

Summary: Pneumonia is a major cause of childhood mortality, with X-ray imaging analysis and machine learning methods like convolutional neural networks being used for diagnosis. However, limitations such as lack of interpretability and insufficient labeled data in medical domains restrict the impact of these systems.

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE (2021)

Article Computer Science, Information Systems

Convolutional neural networks for the classification of chest X-rays in the IoT era

Khaled Almezhghwi et al.

Summary: The study proposes two artificial intelligence approaches utilizing deep learning for the classification of chest X-ray images. These methods, based on the AlexNet model and VGGNet16 method, can accurately identify lung diseases.

MULTIMEDIA TOOLS AND APPLICATIONS (2021)

Article Biochemical Research Methods

Enhanced Diagnosis of Pneumothorax with an Improved Real-Time Augmentation for Imbalanced Chest X-rays Data Based on DCNN

Yaqi Wang et al.

Summary: This paper proposes an image classification algorithm based on deep convolutional neural network for high-resolution medical image analysis of pneumothorax X-rays. The experimental results demonstrate that the method effectively increases the correct diagnosis rate of pneumothorax, with AUC values of 0.9844 and 0.9906 on test data sets. Additionally, a large number of pleural samples were visualized and analyzed based on the experimental results and algorithm's deep learning characteristics, verifying the validity of feature extraction for the network.

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2021)

Article Medical Informatics

Classification of COVID-19 chest X-rays with deep learning: new models or fine tuning?

Tuan D. Pham

Summary: This study investigated the fine tuning of pretrained convolutional neural networks for COVID-19 classification using chest X-rays. Three pretrained CNNs achieved high classification results without data augmentation, with AlexNet, GoogleNet, and SqueezeNet requiring the least training time among pretrained DL models. These findings contribute to the urgent need for deploying AI tools in the public domain for rapid implementation during the pandemic.

HEALTH INFORMATION SCIENCE AND SYSTEMS (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

Knee menisci segmentation and relaxometry of 3D ultrashort echo time cones MR imaging using attention U-Net with transfer learning

Michal Byra et al.

MAGNETIC RESONANCE IN MEDICINE (2020)

Article Radiology, Nuclear Medicine & Medical Imaging

Deep learning modeling using normal mammograms for predicting breast cancer risk

Dooman Arefan et al.

MEDICAL PHYSICS (2020)

Article Pharmacology & Pharmacy

Predicting Inpatient Medication Orders From Electronic Health Record Data

Kathryn Rough et al.

CLINICAL PHARMACOLOGY & THERAPEUTICS (2020)

Article Computer Science, Interdisciplinary Applications

Identifying sub-phenotypes of acute kidney injury using structured and unstructured electronic health record data with memory networks

Zhenxing Xu et al.

JOURNAL OF BIOMEDICAL INFORMATICS (2020)

Article Biotechnology & Applied Microbiology

Titrating gene expression using libraries of systematically attenuated CRISPR guide RNAs

Marco Jost et al.

NATURE BIOTECHNOLOGY (2020)

Article Radiology, Nuclear Medicine & Medical Imaging

User-controlled pipelines for feature integration and head and neck radiation therapy outcome predictions

Mattea L. Welch et al.

PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS (2020)

Article Computer Science, Artificial Intelligence

Deep reinforcement learning for imbalanced classification

Enlu Lin et al.

APPLIED INTELLIGENCE (2020)

Article Cardiac & Cardiovascular Systems

Validation of deep-learning image reconstruction for coronary computed tomography angiography: Impact on noise, image quality and diagnostic accuracy

Dominik C. Benz et al.

JOURNAL OF CARDIOVASCULAR COMPUTED TOMOGRAPHY (2020)

Article Cardiac & Cardiovascular Systems

A morphology based deep learning model for atrial fibrillation detection using single cycle electrocardiographic samples

Sarah W. E. Baalman et al.

INTERNATIONAL JOURNAL OF CARDIOLOGY (2020)

Article Radiology, Nuclear Medicine & Medical Imaging

Dosimetric evaluation of synthetic CT generated with GANs for MRI-only proton therapy treatment planning of brain tumors

Samaneh Kazemifar et al.

JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS (2020)

Article Computer Science, Artificial Intelligence

Detecting thoracic diseases via representation learning with adaptive sampling *

Hao Wang et al.

NEUROCOMPUTING (2020)

Article Biochemistry & Molecular Biology

Deep learning based prediction of species-specific protein S-glutathionylation sites

Shihua Li et al.

BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS (2020)

Article Computer Science, Artificial Intelligence

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

Alejandro Barredo Arrieta et al.

INFORMATION FUSION (2020)

Article Engineering, Biomedical

Assessing and mitigating the effects of class imbalance in machine learning with application to X-ray imaging

Wendi Qu et al.

INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY (2020)

Article Computer Science, Artificial Intelligence

PadChest: A large chest x-ray image dataset with multi-label annotated reports

Aurelia Bustos et al.

MEDICAL IMAGE ANALYSIS (2020)

Article Computer Science, Information Systems

MarsNet: Multi-Label Classification Network for Images of Various Sizes

Ju-Youn Park et al.

IEEE ACCESS (2020)

Article Computer Science, Artificial Intelligence

REMEDIAL-HwR: Tackling multilabel imbalance through label decoupling and data resampling hybridization

Francisco Charte et al.

NEUROCOMPUTING (2019)

Article Computer Science, Interdisciplinary Applications

Synthesizing Chest X-Ray Pathology for Training Deep Convolutional Neural Networks

Hojjat Salehinejad et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2019)

Article Multidisciplinary Sciences

Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification

Ivo M. Baltruschat et al.

SCIENTIFIC REPORTS (2019)

Review Biochemical Research Methods

Machine and deep learning meet genome-scale metabolic modeling

Guido Zampieri et al.

PLOS COMPUTATIONAL BIOLOGY (2019)

Article Computer Science, Information Systems

A Novel Approach for Multi-Label Chest X-Ray Classification of Common Thorax Diseases

Imane Allaouzi et al.

IEEE ACCESS (2019)

Article Automation & Control Systems

LiftingNet: A Novel Deep Learning Network With Layerwise Feature Learning From Noisy Mechanical Data for Fault Classification

Jun Pan et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2018)

Proceedings Paper Computer Science, Artificial Intelligence

Densely Connected Convolutional Networks

Gao Huang et al.

30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)

Proceedings Paper Computer Science, Artificial Intelligence

Xception: Deep Learning with Depthwise Separable Convolutions

Francois Chollet

30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)

Article Computer Science, Artificial Intelligence

MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation

Francisco Charte et al.

KNOWLEDGE-BASED SYSTEMS (2015)

Review Multidisciplinary Sciences

Deep learning

Yann LeCun et al.

NATURE (2015)