Pneumonia is a leading cause of mortality in children under five, requiring accurate chest X-ray diagnosis. This study presents a machine learning-based Pediatric Chest Pneumonia Classification System to assist healthcare professionals in diagnosing pneumonia from chest X-ray images. The CNN-based model was trained on 5,863 labeled chest X-ray images from children aged 0-5 years from the Guangzhou Women and Children's Medical Center. To address limited data, we applied augmentation techniques (rotation, zooming, shear, horizontal flipping) and employed GANs to generate synthetic images, addressing class imbalance. The system achieved optimal performance using combined original, augmented, and GAN-generated data, evaluated through accuracy and F1 score metrics. The final model was deployed via a Flask web application, enabling real-time classification with probability estimates. Results demonstrate the potential of deep learning and GANs in improving diagnostic accuracy and efficiency for pediatric pneumonia classification, particularly valuable in resource-limited clinical settings https://github.com/AbdulManaf12/Pediatric-Chest-Pneumonia-Classification
Intellogic Limited Liability Company (Intellogic LLC)（Intellogic有限责任公司（Intellogic LLC））；ITMO University（圣彼得堡国立信息技术机械与光学大学）
We propose a novel method to improve deep learning model performance on highly-imbalanced tasks. The proposed method is based on CycleGAN to achieve balanced dataset. We show that data augmentation with GAN helps to improve accuracy of pneumonia binary classification task even if the generative network was trained on the same training dataset.
Institute of Medical Science, University of Toronto（多伦多大学医学科学研究所）；The Hospital for Sick Children（病童医院）；University Health Network（大学健康网络）；Department of Mechanical and Industrial Engineering, University of Toronto（多伦多大学机械与工业工程系）
Successful training of convolutional neural networks (CNNs) requires a substantial amount of data. With small datasets networks generalize poorly. Data Augmentation techniques improve the generalizability of neural networks by using existing training data more effectively. Standard data augmentation methods, however, produce limited plausible alternative data. Generative Adversarial Networks (GANs) have been utilized to generate new data and improve the performance of CNNs. Nevertheless, data augmentation techniques for training GANs are under-explored compared to CNNs. In this work, we propos…
Pneumonia App: a mobile application for efficient pediatric pneumonia diagnosis using explainable convolutional neural networks (CNN)
Zhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, The Fifth Affiliated Hospital of Wenzhou Medical University（温州医科大学附属第五医院）；Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院清华-伯克利深圳学院）；Institute of Biopharmaceutical and Health Engineering, Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院生物医药与健康工程研究院）；Key Lab for Industrial Biocatalysis, Ministry of Education, Department of Chemical Engineering, Tsinghua University（清华大学化学工程系工业生物催化教育部重点实验室）；State Key Laboratory of Urban Water Resources and Environment, School of Civil & Environmental Engineering, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）土木与环境工程学院城市水资源与水环境国家重点实验室）；Department of Radiology, The University of Hong Kong - Shenzhen Hospital（香港大学深圳医院放射科）
University of Toronto（多伦多大学）；Vector Institute（向量研究所）；The Hospital for Sick Children（病童医院）
Early detection of cancer is key to a good prognosis and requires frequent testing, especially in pediatrics. Whole-body magnetic resonance imaging (wbMRI) is an essential part of several well-established screening protocols, with screening starting in early childhood. To date, machine learning (ML) has been used on wbMRI images to stage adult cancer patients. It is not possible to use such tools in pediatrics due to the changing bone signal throughout growth, the difficulty of obtaining these images in young children due to movement and limited compliance, and the rarity of positive cases. We…
National Institutes of Health（美国国立卫生研究院）；Ping An Technology Co., Ltd.（平安科技有限公司）
In this work, we exploit the unsupervised domain adaptation problem for radiology image interpretation across domains. Specifically, we study how to adapt the disease recognition model from a labeled source domain to an unlabeled target domain, so as to reduce the effort of labeling each new dataset. To address the shortcoming of cross-domain, unpaired image-to-image translation methods which typically ignore class-specific semantics, we propose a task-driven, discriminatively trained, cycle-consistent generative adversarial network, termed TUNA-Net. It is able to preserve 1) low-level details…
An accurate and prompt diagnosis of pediatric pneumonia is imperative for successful treatment intervention. One approach to diagnose pneumonia cases is using radiographic data. In this article, we propose a novel parsimonious scalar-on-image classification model adopting the ideas of functional data analysis. Our main idea is to treat images as functional measurements and exploit underlying covariance structures to select basis functions; these bases are then used in approximating both image profiles and corresponding regression coefficient. We re-express the regression model into a standard …
Pneumonia is a lung infection that causes 15% of childhood mortality, over 800,000 children under five every year, all over the world. This pathology is mainly caused by viruses or bacteria. X-rays imaging analysis is one of the most used methods for pneumonia diagnosis. These clinical images can be analyzed using machine learning methods such as convolutional neural networks (CNN), which learn to extract critical features for the classification. However, the usability of these systems is limited in medicine due to the lack of interpretability, because of these models cannot be used to generat…
SharpXR: Structure-Aware Denoising for Pediatric Chest X-Rays
Federal University of Agriculture Abeokuta, Nigeria（尼日利亚阿贝奥库塔联邦农业大学）；University of Lagos, Nigeria（尼日利亚拉各斯大学）；University of Nigeria, Nsukka（尼日利亚恩苏克卡大学）；University of Benin, Nigeria（尼日利亚贝宁大学）；Olabisi Onabanjo University, Nigeria（尼日利亚奥拉比·奥纳班乔大学）；Federal University of Technology, Minna, Nigeria（尼日利亚米纳联邦技术大学）；McGill University, Canada（加拿大麦吉尔大学）；AGH University of Krakow, Poland（波兰克拉科夫AGH大学）
Intelligent Medical Image Computing Systems (IMICS) Lab（智能医学影像计算系统实验室）；The Hospital for Sick Children（病童医院）；University of Toronto（多伦多大学）
COVID-19 spread across the globe at an immense rate has left healthcare systems incapacitated to diagnose and test patients at the needed rate. Studies have shown promising results for detection of COVID-19 from viral bacterial pneumonia in chest X-rays. Automation of COVID-19 testing using medical images can speed up the testing process of patients where health care systems lack sufficient numbers of the reverse-transcription polymerase chain reaction (RT-PCR) tests. Supervised deep learning models such as convolutional neural networks (CNN) need enough labeled data for all classes to correct…
University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）；National Institutes of Health Clinical Center（美国国立卫生研究院临床中心）；Networking Health（网络健康）
COVID-19 is a novel infectious disease responsible for over 800K deaths worldwide as of August 2020. The need for rapid testing is a high priority and alternative testing strategies including X-ray image classification are a promising area of research. However, at present, public datasets for COVID19 x-ray images have low data volumes, making it challenging to develop accurate image classifiers. Several recent papers have made use of Generative Adversarial Networks (GANs) in order to increase the training data volumes. But realistic synthetic COVID19 X-rays remain challenging to generate. We p…
Harmonization Across Imaging Locations(HAIL): One-Shot Learning for Brain MRI
Children’s National Hospital（美国儿童国家医疗中心）；George Washington University（乔治·华盛顿大学）；Children’s Hospital Colorado（科罗拉多儿童医院）；Children’s Hospital of Philadelphia（费城儿童医院）
Magnetic resonance image (MRI) reconstruction is a severely ill-posed linear inverse task demanding time and resource intensive computations that can substantially trade off {\it accuracy} for {\it speed} in real-time imaging. In addition, state-of-the-art compressed sensing (CS) analytics are not cognizant of the image {\it diagnostic quality}. To cope with these challenges we put forth a novel CS framework that permeates benefits from generative adversarial networks (GAN) to train a (low-dimensional) manifold of diagnostic-quality MR images from historical patients. Leveraging a mixture of l…
Cross-dataset transportability of pediatric chest X-ray deep learning across three countries: discrimination, calibration, operating-point failure, and limited-label recovery
American International University-Bangladesh (AIUB)（美国国际大学孟加拉分校）
National Library of Medicine, National Institutes of Health（美国国家医学图书馆，美国国立卫生研究院）
Medical images commonly exhibit multiple abnormalities. Predicting them requires multi-class classifiers whose training and desired reliable performance can be affected by a combination of factors, such as, dataset size, data source, distribution, and the loss function used to train the deep neural networks. Currently, the cross-entropy loss remains the de-facto loss function for training deep learning classifiers. This loss function, however, asserts equal learning from all classes, leading to a bias toward the majority class. In this work, we benchmark various state-of-the-art loss functions…
