Deep Learning Method for Automated Classification of Anteroposterior and Posteroanterior Chest Radiographs

Kim, Tae Kyung; Yi, Paul H.; Wei, Jinchi; Shin, Ji Won; Hager, Gregory D.; Hui, Ferdinand; Sair, Haris I.; Lin, Cheng Ting

doi:10.1007/s10278-019-00208-0

Cited by 30 publications

(29 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This enables the usage of grayscale image inputs instead of artificially created RGB images. Overall, both studies show and prove the potential of deep learning for the validation of meta information within the clinical routine [19]. In addition to the results and related studies, the limitations of our study must also be considered.…”

Section: Discussionmentioning

confidence: 56%

Differentiation Between Anteroposterior and Posteroanterior Chest X-Ray View Position With Convolutional Neural Networks

Hosch

Kroll

Nensa

et al. 2020

Rofo

View full text Add to dashboard Cite

Purpose Detection and validation of the chest X-ray view position with use of convolutional neural networks to improve meta-information for data cleaning within a hospital data infrastructure. Material and Methods Within this paper we developed a convolutional neural network which automatically detects the anteroposterior and posteroanterior view position of a chest radiograph. We trained two different network architectures (VGG variant and ResNet-34) with data published by the RSNA (26 684 radiographs, class distribution 46 % AP, 54 % PA) and validated these on a self-compiled dataset with data from the University Hospital Essen (4507, radiographs, class distribution 55 % PA, 45 % AP) labeled by a human reader. For visualization and better understanding of the network predictions, a Grad-CAM was generated for each network decision. The network results were evaluated based on the accuracy, the area under the curve (AUC), and the F1-score against the human reader labels. Also a final performance comparison between model predictions and DICOM labels was performed. Results The ensemble models reached accuracy and F1-scores greater than 95 %. The AUC reaches more than 0.99 for the ensemble models. The Grad-CAMs provide insight as to which anatomical structures contributed to a decision by the networks which are comparable with the ones a radiologist would use. Furthermore, the trained models were able to generalize over mislabeled examples, which was found by comparing the human reader labels to the predicted labels as well as the DICOM labels. Conclusion The results show that certain incorrectly entered meta-information of radiological images can be effectively corrected by deep learning in order to increase data quality in clinical application as well as in research. Key Points: Citation Format

show abstract

Section: Discussionmentioning

confidence: 56%

Differentiation Between Anteroposterior and Posteroanterior Chest X-Ray View Position With Convolutional Neural Networks

Hosch

Kroll

Nensa

et al. 2020

Rofo

View full text Add to dashboard Cite

show abstract

“…Performance was better when validated on internal datasets rather than external ones (using subset of dataset used for training) by Ho et al [ 133 ]; however, there is an optimal training dataset size for each situation. Pediatrics were introduced to chest diseases diagnosis as suggested by Candemir and Antani [ 7 ]; then, the model was trained with pediatrics and adults images resulting with good performance of the deep learning classifier by Kim et al [ 171 ]. However, the addition of adults images to training data did not increase the accuracy a lot (only increased 1.6%).…”

Section: Discussionmentioning

confidence: 99%

“…CXRs classification into anteroposterior or posteroanterior views was held by Kim et al [171] who developed a ResNet-18 DCNN. It was trained on NIH ChestXray14 database that consists of adults and pediatrics CXRs.…”

Section: General Thoracic Diseasesmentioning

confidence: 99%

“…Few contributions to chest were based on auto-encoders such as those of Xie et al [202] and Nemoto et al [163], while many were based on U-nets such as segmentation approaches of Ahn et al [88] and Furutani et al [103]. Likewise, other CNN architectures were used as FCNN by Dou et al [143], ResNet by Kim et al [171], and DenseNet by Gozes and Greenspan [166]. Moreover, combinations of architectures took place as by Gozes and Greenspan [164] where ResNet and DenseNet were integrated, and comparisons of multiple architectures were explicit as by Gozes and Greenspan [133], Ayan and Unver [131], Pan et al [62], and Wang et al [184].…”

Section: Network Architecturementioning

confidence: 99%

See 1 more Smart Citation

Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

Farhat

Sakr

Kilany

2020

Machine Vision and Applications

View full text Add to dashboard Cite

Shortly after deep learning algorithms were applied to Image Analysis, and more importantly to medical imaging, their applications increased significantly to become a trend. Likewise, deep learning applications (DL) on pulmonary medical images emerged to achieve remarkable advances leading to promising clinical trials. Yet, coronavirus can be the real trigger to open the route for fast integration of DL in hospitals and medical centers. This paper reviews the development of deep learning applications in medical image analysis targeting pulmonary imaging and giving insights of contributions to COVID-19. It covers more than 160 contributions and surveys in this field, all issued between February 2017 and May 2020 inclusively, highlighting various deep learning tasks such as classification, segmentation, and detection, as well as different pulmonary pathologies like airway diseases, lung cancer, COVID-19 and other infections. It summarizes and discusses the current state-of-the-art approaches in this research domain, highlighting the challenges, especially with COVID-19 pandemic current situation.

show abstract

“…Ultrasound image data focusing on pelvic, cardiac, lung, abdominal, musculoskeletal, central vascular access, and ocular examinations was obtained from public domain, open sources with no accompanying patient information. Compared to large public domain radiology imaging databases for chest X‐rays, head, and body CT images, no such ultrasound databases are currently available 11‐13 . Ultrasound videos, which are commonly obtained in POCUS scanning for education, yield large numbers of individual images that can be used for convolutional neural network training.…”

Section: Methodsmentioning

confidence: 99%

DIY AI, deep learning network development for automated image classification in a point‐of‐care ultrasound quality assurance program

2020

View full text Add to dashboard Cite

Presented at 2019 ACEP Scientific Assembly at #344. Funding and support: By JACEP Open policy, all authors are required to disclose any and all commercial, financial, and other relationships in any way related to the subject of this article as per ICMJE conflict of interest guidelines (see www.icmje.org). The authors have stated that no such relationships exist. AbstractBackground: Artificial intelligence (AI) is increasingly a part of daily life and offers great possibilities to enrich health care. Imaging applications of AI have been mostly developed by large, well-funded companies and currently are inaccessible to the comparatively small market of point-of-care ultrasound (POCUS) programs. Given this absence of commercial solutions, we sought to create and test a do-it-yourself (DIY) deep learning algorithm to classify ultrasound images to enhance the quality assurance work-flow for POCUS programs. Methods:We created a convolutional neural network using publicly available software tools and pre-existing convolutional neural network architecture. The convolutional neural network was subsequently trained using ultrasound images from seven ultrasound exam types: pelvis, heart, lung, abdomen, musculoskeletal, ocular, and central vascular access from 189 publicly available POCUS videos. Approximately 121,000 individual images were extracted from the videos, 80% were used for model training and 10% each for cross validation and testing. We then tested the algorithm for accuracy against a set of 160 randomly extracted ultrasound frames from ultrasound videos not previously used for training and that were performed on different ultrasound equipment. Three POCUS experts blindly categorized the 160 random images, and results were compared to the convolutional neural network algorithm. Descriptive statistics and Krippendorff alpha reliability estimates were calculated. Results:The cross validation of the convolutional neural network approached 99% for accuracy. The algorithm accurately classified 98% of the test ultrasound images.In the new POCUS program simulation phase, the algorithm accurately classified 70% of 160 new images for moderate correlation with the ground truth, = 0.64. The three blinded POCUS experts correctly classified 93%, 94%, and 98% of the images,

show abstract

Deep Learning Method for Automated Classification of Anteroposterior and Posteroanterior Chest Radiographs

Cited by 30 publications

References 16 publications

Differentiation Between Anteroposterior and Posteroanterior Chest X-Ray View Position With Convolutional Neural Networks

Differentiation Between Anteroposterior and Posteroanterior Chest X-Ray View Position With Convolutional Neural Networks

Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

DIY AI, deep learning network development for automated image classification in a point‐of‐care ultrasound quality assurance program

Contact Info

Product

Resources

About