Utilization Trends in Noncardiac Thoracic Imaging, 2002-2014

Kamel, Sarah; Levin, David C.; Parker, Laurence; Rao, Vijay M.

doi:10.1016/j.jacr.2016.09.039

Cited by 26 publications

(22 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Chest X-ray is the most common medical imaging exam with over 35 million taken every year in the US alone [2]. They allow for inexpensive screening of several pathologies including masses, pulmonary nodules, effusions, cardiac abnormalities and pneumothorax.…”

Section: Introductionmentioning

confidence: 99%

Learning to Recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks

Gündel

Grbić

Georgescu

et al. 2019

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

108

134

View full text Add to dashboard Cite

Chest X-ray is the most common medical imaging exam used to assess multiple pathologies. Automated algorithms and tools have the potential to support the reading workflow, improve efficiency, and reduce reading errors. With the availability of large scale data sets, several methods have been proposed to classify pathologies on chest X-ray images. However, most methods report performance based on random image based splitting, ignoring the high probability of the same patient appearing in both training and test set. In addition, most methods fail to explicitly incorporate the spatial information of abnormalities or utilize the high resolution images. We propose a novel approach based on location aware Dense Networks (DNetLoc), whereby we incorporate both high-resolution image data and spatial information for abnormality classification. We evaluate our method on the largest data set reported in the community, containing a total of 86,876 patients and 297,541 chest X-ray images. We achieve (i) the best average AUC score for published training and test splits on the single benchmarking data set (ChestX-Ray14 [1]), and (ii) improved AUC scores when the pathology location information is explicitly used. To foster future research we demonstrate the limitations of the current benchmarking setup [1] and provide new reference patient-wise splits for the used data sets. This could support consistent and meaningful benchmarking of future methods on the largest publicly available data sets.

show abstract

Section: Introductionmentioning

confidence: 99%

Learning to Recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks

Gündel

Grbić

Georgescu

et al. 2019

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

108

134

View full text Add to dashboard Cite

show abstract

“…The chest X-ray is one of the most commonly accessible examinations for screening and diagnosis of many lung diseases including Infiltration, Effusion, Atelectasis, Nodule, Mass, Pneumothorax, Consolidation, Pleural Thickening, Cardiomegaly, Emphysema, Edema, Fibrosis, Pneumonia and Hernia [1]. In the USA alone, over 35 million chest X ray images are taken every year and radiologists have to read more than 100 X-ray studies in a day [2]. In China, there are much more patients and chest X-ray images due to the large population and increasing health consciousness.…”

Section: Introductionmentioning

confidence: 99%

CXNet-m1: Anomaly Detection on Chest X-Rays With Image-Based Deep Learning

Bie

2019

IEEE Access

View full text Add to dashboard Cite

Detecting anomaly of chest X-ray images by advanced technologies, such as deep learning, is an urgent need to improve the work efficiency and diagnosis accuracy. Fine-tuning existing deep learning networks for medical image processing suffers from over-fitting and low transfer efficiency. To overcome such limitations, we design a hierarchical convolutional neural network (CNN) structure for ChestX-ray14 and propose a new network CXNet-m1, which is much shorter, thinner but more powerful than fine-tuning. We also raise a novel loss function sin-loss, which can learn discriminative information from misclassified and indistinguishable images. Besides, we optimize the convolutional kernels of CXNet-m1 to achieve better classification accuracy. The experimental results show that our light model CXNet-m1 with sin-loss function achieves better accuracy rate, recall rate, F1-score, and AUC value. It illustrates that designing a proper CNN is better than fine-tuning deep networks, and the increase of training data is vital to enhance the performance of CNN. INDEX TERMS Chest X-Rays image, anomaly detection, deep neural network, self-adapting loss function. HAO WU received the B.E. and Ph.D. degrees

show abstract

“…The amount of medical images continues to increase explosively, and the average radiologist reads more than 100 simple radiograph examinations per day in the United States [7,8]. Application of deep learning technologies to radiology can be clinically very beneficial to radiologists and can be a new academic field in radiology.…”

Section: Discussionmentioning

confidence: 99%

“…Because of the advancement in medical imaging techniques over the last century, there has been a tremendous increase in the amount of medical images. Simple radiographies are still the most commonly performed medical imaging until now due to both cost-effectiveness and clinical usefulness [7,8]. Therefore, simple radiographies take a large portion of radiologists' work-loads [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…Simple radiographies are still the most commonly performed medical imaging until now due to both cost-effectiveness and clinical usefulness [7,8]. Therefore, simple radiographies take a large portion of radiologists' work-loads [7][8][9]. In addition, accurate interpretation of simple radiographs requires an extensive amount of medical and radiologic knowledge and experience, as simple radiographies are composed of complex three-dimensional anatomic information projected into two-dimensional images [8].…”

Utilization Trends in Noncardiac Thoracic Imaging, 2002-2014

Cited by 26 publications

References 13 publications

Learning to Recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks

Learning to Recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks

CXNet-m1: Anomaly Detection on Chest X-Rays With Image-Based Deep Learning

Untitled

Contact Info

Product

Resources

About