
@inproceedings{agunwaBodyPartImaging2019,
  title = {Body Part and Imaging Modality Classification for a General Radiology Cognitive Assistant},
  booktitle = {Medical {{Imaging}} 2019: {{Image Processing}}},
  author = {Agunwa, Chinyere and Moradi, Mehdi and Wong, Ken C. L. and {Syeda-Mahmood}, Tanveer},
  year = {2019},
  month = mar,
  volume = {10949},
  pages = {1094910},
  publisher = {{International Society for Optics and Photonics}},
  doi = {10.1117/12.2513074},
  abstract = {Decision support systems built for radiologists need to cover a fairly wide range of image types, with the ability to route each image to the relevant algorithm. Furthermore, the training of such networks requires building large datasets with significant efforts in image curation. In situations where the DICOM tag of an image is unavailable, or unreliable, a classifier that can automatically detect the body part depicted in the image, as well as the imaging modality, is necessary. Previous work has shown the use of imaging and textual features to distinguish between imaging modalities. In this work, we present a model for the simultaneous classification of body part and imaging modality, which to our knowledge has not been done before, as part of the larger work to create a cognitive assistant for radiologists. This classification network consists of 10 classes built from a VGG network architecture using transfer learning to learn generic features. An accuracy of 94.8\% is achieved.},
  file = {/home/work/Zotero/storage/JXL3XA9M/12.2513074.html}
}

@article{chenRethinkingAtrousConvolution2017,
  title = {Rethinking {{Atrous Convolution}} for {{Semantic Image Segmentation}}},
  author = {Chen, Liang-Chieh and Papandreou, George and Schroff, Florian and Adam, Hartwig},
  year = {2017},
  month = dec,
  abstract = {In this work, we revisit atrous convolution, a powerful tool to explicitly adjust filter's field-of-view as well as control the resolution of feature responses computed by Deep Convolutional Neural Networks, in the application of semantic image segmentation. To handle the problem of segmenting objects at multiple scales, we design modules which employ atrous convolution in cascade or in parallel to capture multi-scale context by adopting multiple atrous rates. Furthermore, we propose to augment our previously proposed Atrous Spatial Pyramid Pooling module, which probes convolutional features at multiple scales, with image-level features encoding global context and further boost performance. We also elaborate on implementation details and share our experience on training our system. The proposed `DeepLabv3' system significantly improves over our previous DeepLab versions without DenseCRF post-processing and attains comparable performance with other state-of-art models on the PASCAL VOC 2012 semantic image segmentation benchmark.},
  archivePrefix = {arXiv},
  eprint = {1706.05587},
  eprinttype = {arxiv},
  file = {/home/work/Zotero/storage/VBL3YJ25/Chen et al_2017_Rethinking Atrous Convolution for Semantic Image Segmentation.pdf;/home/work/Zotero/storage/LBIUXSTM/1706.html},
  journal = {arXiv:1706.05587 [cs]},
  keywords = {⛔ No DOI found,Computer Science - Computer Vision and Pattern Recognition},
  primaryClass = {cs}
}

@article{choyCurrentApplicationsFuture2018,
  title = {Current {{Applications}} and {{Future Impact}} of {{Machine Learning}} in {{Radiology}}},
  author = {Choy, Garry and Khalilzadeh, Omid and Michalski, Mark and Do, Synho and Samir, Anthony E. and Pianykh, Oleg S. and Geis, J. Raymond and Pandharipande, Pari V. and Brink, James A. and Dreyer, Keith J.},
  year = {2018},
  month = jun,
  volume = {288},
  pages = {318--328},
  publisher = {{Radiological Society of North America}},
  issn = {0033-8419},
  doi = {10.1148/radiol.2018171820},
  abstract = {Recent advances and future perspectives of machine learning techniques offer promising applications in medical imaging. Machine learning has the potential to improve different steps of the radiology workflow including order scheduling and triage, clinical decision support systems, detection and interpretation of findings, postprocessing and dose estimation, examination quality control, and radiology reporting. In this article, the authors review examples of current applications of machine learning and artificial intelligence techniques in diagnostic radiology. In addition, the future impact and natural extension of these techniques in radiology practice are discussed.\textcopyright{} RSNA, 2018},
  file = {/home/work/Zotero/storage/TGRBLQ68/Choy et al_2018_Current Applications and Future Impact of Machine Learning in Radiology.pdf;/home/work/Zotero/storage/6UMMICEQ/radiol.html},
  journal = {Radiology},
  number = {2}
}

@article{essesAutomatedImageQuality2018,
  title = {Automated Image Quality Evaluation of {{T2}}-Weighted Liver {{MRI}} Utilizing Deep Learning Architecture},
  author = {Esses, Steven J. and Lu, Xiaoguang and Zhao, Tiejun and Shanbhogue, Krishna and Dane, Bari and Bruno, Mary and Chandarana, Hersh},
  year = {2018},
  volume = {47},
  pages = {723--728},
  issn = {1522-2586},
  doi = {10.1002/jmri.25779},
  abstract = {Purpose To develop and test a deep learning approach named Convolutional Neural Network (CNN) for automated screening of T2-weighted (T2WI) liver acquisitions for nondiagnostic images, and compare this automated approach to evaluation by two radiologists. Materials and Methods We evaluated 522 liver magnetic resonance imaging (MRI) exams performed at 1.5T and 3T at our institution between November 2014 and May 2016 for CNN training and validation. The CNN consisted of an input layer, convolutional layer, fully connected layer, and output layer. 351 T2WI were anonymized for training. Each case was annotated with a label of being diagnostic or nondiagnostic for detecting lesions and assessing liver morphology. Another independently collected 171 cases were sequestered for a blind test. These 171 T2WI were assessed independently by two radiologists and annotated as being diagnostic or nondiagnostic. These 171 T2WI were presented to the CNN algorithm and image quality (IQ) output of the algorithm was compared to that of two radiologists. Results There was concordance in IQ label between Reader 1 and CNN in 79\% of cases and between Reader 2 and CNN in 73\%. The sensitivity and the specificity of the CNN algorithm in identifying nondiagnostic IQ was 67\% and 81\% with respect to Reader 1 and 47\% and 80\% with respect to Reader 2. The negative predictive value of the algorithm for identifying nondiagnostic IQ was 94\% and 86\% (relative to Readers 1 and 2). Conclusion We demonstrate a CNN algorithm that yields a high negative predictive value when screening for nondiagnostic T2WI of the liver. Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:723\textendash 728.},
  annotation = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/jmri.25779},
  copyright = {\textcopyright{} 2017 International Society for Magnetic Resonance in Medicine},
  file = {/home/work/Zotero/storage/98RVCZN8/Esses et al_2018_Automated image quality evaluation of T2-weighted liver MRI utilizing deep.pdf;/home/work/Zotero/storage/DR9V4FQB/jmri.html},
  journal = {Journal of Magnetic Resonance Imaging},
  language = {en},
  number = {3}
}

@article{essesAutomatedImageQuality2018a,
  title = {Automated Image Quality Evaluation of {{T2}} -Weighted Liver {{MRI}} Utilizing Deep Learning Architecture},
  author = {Esses, Steven J. and Lu, Xiaoguang and Zhao, Tiejun and Shanbhogue, Krishna and Dane, Bari and Bruno, Mary and Chandarana, Hersh},
  year = {2018},
  month = mar,
  volume = {47},
  pages = {723--728},
  issn = {1522-2586},
  doi = {10.1002/jmri.25779},
  abstract = {PURPOSE: To develop and test a deep learning approach named Convolutional Neural Network (CNN) for automated screening of T2 -weighted (T2 WI) liver acquisitions for nondiagnostic images, and compare this automated approach to evaluation by two radiologists. MATERIALS AND METHODS: We evaluated 522 liver magnetic resonance imaging (MRI) exams performed at 1.5T and 3T at our institution between November 2014 and May 2016 for CNN training and validation. The CNN consisted of an input layer, convolutional layer, fully connected layer, and output layer. 351\,T2 WI were anonymized for training. Each case was annotated with a label of being diagnostic or nondiagnostic for detecting lesions and assessing liver morphology. Another independently collected 171 cases were sequestered for a blind test. These 171\,T2 WI were assessed independently by two radiologists and annotated as being diagnostic or nondiagnostic. These 171\,T2 WI were presented to the CNN algorithm and image quality (IQ) output of the algorithm was compared to that of two radiologists. RESULTS: There was concordance in IQ label between Reader 1 and CNN in 79\% of cases and between Reader 2 and CNN in 73\%. The sensitivity and the specificity of the CNN algorithm in identifying nondiagnostic IQ was 67\% and 81\% with respect to Reader 1 and 47\% and 80\% with respect to Reader 2. The negative predictive value of the algorithm for identifying nondiagnostic IQ was 94\% and 86\% (relative to Readers 1 and 2). CONCLUSION: We demonstrate a CNN algorithm that yields a high negative predictive value when screening for nondiagnostic T2 WI of the liver. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:723-728.},
  file = {/home/work/Zotero/storage/H4F9LKR4/Esses et al_2018_Automated image quality evaluation of T2 -weighted liver MRI utilizing deep.pdf},
  journal = {Journal of magnetic resonance imaging: JMRI},
  language = {eng},
  number = {3},
  pmid = {28577329}
}

@article{fangGeneralizedRadiographicView2020,
  title = {Generalized {{Radiographic View Identification}} with {{Deep Learning}}},
  author = {Fang, Xiang and Harris, Leah and Zhou, Wei and Huo, Donglai},
  year = {2020},
  month = dec,
  issn = {1618-727X},
  doi = {10.1007/s10278-020-00408-z},
  abstract = {To explore the feasibility of an automatic machine-learning algorithm-based quality control system for the practice of diagnostic radiography, performance of a~convolutional neural networks (CNN)-based algorithm for identifying radiographic (X-ray) views at different levels was examined with a retrospective, HIPAA-compliant, and IRB-approved study performed on 15,046 radiographic images acquired between 2013 and 2018 from nine clinical sites affiliated with our institution. Images were labeled according to four classification levels: level 1 (anatomy level, 25 classes), level 2 (laterality level, 41 classes), level 3 (projection level, 108 classes), and level 4 (detailed level, 143 classes). An Inception V3 model pre-trained with ImageNet dataset was trained with transfer learning to classify the image at all levels. Sensitivity and positive predictive value were reported for each class, and overall accuracy was reported for each level. Accuracy was also reported when we allowed for ``reasonable errors''. The overall accuracy was 0.96, 0.93, 0.90, and 0.86 at levels 1, 2, 3, and 4, respectively. Overall accuracy increased to 0.99, 0.97, 0.94, and 0.88 when ``reasonable errors'' were allowed. Machine learning algorithms resulted in reasonable model performance for identifying radiographic views with acceptable accuracy when ``reasonable errors'' were allowed. Our findings demonstrate the feasibility of building a quality-control program based on machine-learning algorithms to identify radiographic views with acceptable accuracy at lower levels, which could be applied in a clinical setting.},
  file = {/home/work/Zotero/storage/KGEJXTBW/Fang et al_2020_Generalized Radiographic View Identification with Deep Learning.pdf},
  journal = {Journal of Digital Imaging},
  language = {en}
}

@article{fangGeneralizedRadiographicView2020a,
  title = {Generalized {{Radiographic View Identification}} with {{Deep Learning}}},
  author = {Fang, Xiang and Harris, Leah and Zhou, Wei and Huo, Donglai},
  year = {2020},
  month = dec,
  issn = {1618-727X},
  doi = {10.1007/s10278-020-00408-z},
  abstract = {To explore the feasibility of an automatic machine-learning algorithm-based quality control system for the practice of diagnostic radiography, performance of a~convolutional neural networks (CNN)-based algorithm for identifying radiographic (X-ray) views at different levels was examined with a retrospective, HIPAA-compliant, and IRB-approved study performed on 15,046 radiographic images acquired between 2013 and 2018 from nine clinical sites affiliated with our institution. Images were labeled according to four classification levels: level 1 (anatomy level, 25 classes), level 2 (laterality level, 41 classes), level 3 (projection level, 108 classes), and level 4 (detailed level, 143 classes). An Inception V3 model pre-trained with ImageNet dataset was trained with transfer learning to classify the image at all levels. Sensitivity and positive predictive value were reported for each class, and overall accuracy was reported for each level. Accuracy was also reported when we allowed for ``reasonable errors''. The overall accuracy was 0.96, 0.93, 0.90, and 0.86 at levels 1, 2, 3, and 4, respectively. Overall accuracy increased to 0.99, 0.97, 0.94, and 0.88 when ``reasonable errors'' were allowed. Machine learning algorithms resulted in reasonable model performance for identifying radiographic views with acceptable accuracy when ``reasonable errors'' were allowed. Our findings demonstrate the feasibility of building a quality-control program based on machine-learning algorithms to identify radiographic views with acceptable accuracy at lower levels, which could be applied in a clinical setting.},
  file = {/home/work/Zotero/storage/C5V6C7L5/Fang et al_2020_Generalized Radiographic View Identification with Deep Learning.pdf},
  journal = {Journal of Digital Imaging},
  language = {en}
}

@article{fesharakiMedicalXrayImage2013,
  title = {Medical {{X}}-Ray {{Image Hierarchical Classification Using}} a {{Merging}} and {{Splitting Scheme}} in {{Feature Space}}},
  author = {Fesharaki, Nooshin Jafari and Pourghassem, Hossein},
  year = {2013},
  volume = {3},
  pages = {150--163},
  issn = {2228-7477},
  abstract = {Due to the daily mass production and the widespread variation of medical X-ray images, it is necessary to classify these for searching and retrieving proposes, especially for content-based medical image retrieval systems. In this paper, a medical X-ray image hierarchical classification structure based on a novel merging and splitting scheme and using shape and texture features is proposed. In the first level of the proposed structure, to improve the classification performance, similar classes with regard to shape contents are grouped based on merging measures and shape features into the general overlapped classes. In the next levels of this structure, the overlapped classes split in smaller classes based on the classification performance of combination of shape and texture features or texture features only. Ultimately, in the last levels, this procedure is also continued forming all the classes, separately. Moreover, to optimize the feature vector in the proposed structure, we use orthogonal forward selection algorithm according to Mahalanobis class separability measure as a feature selection and reduction algorithm. In other words, according to the complexity and inter-class distance of each class, a sub-space of the feature space is selected in each level and then a supervised merging and splitting scheme is applied to form the hierarchical classification. The proposed structure is evaluated on a database consisting of 2158 medical X-ray images of 18 classes (IMAGECLEF 2005 database) and accuracy rate of 93.6\% in the last level of the hierarchical structure for an 18-class classification problem is obtained.},
  journal = {Journal of Medical Signals and Sensors},
  number = {3},
  pmcid = {PMC3959005},
  pmid = {24672763}
}

@inproceedings{heDeepResidualLearning2016,
  ids = {heDeepResidualLearning2015},
  title = {Deep {{Residual Learning}} for {{Image Recognition}}},
  booktitle = {2016 {{IEEE Conference}} on {{Computer Vision}} and {{Pattern Recognition}} ({{CVPR}})},
  author = {He, K. and Zhang, X. and Ren, S. and Sun, J.},
  year = {2016},
  month = jun,
  pages = {770--778},
  issn = {1063-6919},
  doi = {10.1109/CVPR.2016.90},
  abstract = {Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\texttimes{} deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57\% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28\% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC \& COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.},
  archivePrefix = {arXiv},
  eprint = {1512.03385},
  eprinttype = {arxiv},
  file = {/home/work/Zotero/storage/GMB76IYH/He et al_2016_Deep Residual Learning for Image Recognition.pdf;/home/work/Zotero/storage/WN8JDUJR/7780459.html},
  keywords = {⛔ No DOI found,Computer Science - Computer Vision and Pattern Recognition}
}

@misc{ImprovingBreastCancer,
  title = {Improving {{Breast Cancer Detection Accuracy}} of {{Mammography}} with the {{Concurrent Use}} of an {{Artificial Intelligence Tool}} | {{Radiology}}: {{Artificial Intelligence}}},
  file = {/home/work/Zotero/storage/8MXHG8YJ/Improving Breast Cancer Detection Accuracy of Mammography with the Concurrent.pdf;/home/work/Zotero/storage/MWZ8R3B5/ryai.html},
  howpublished = {https://pubs.rsna.org/doi/10.1148/ryai.2020190208}
}

@article{lindseyDeepNeuralNetwork2018,
  title = {Deep Neural Network Improves Fracture Detection by Clinicians},
  author = {Lindsey, Robert and Daluiski, Aaron and Chopra, Sumit and Lachapelle, Alexander and Mozer, Michael and Sicular, Serge and Hanel, Douglas and Gardner, Michael and Gupta, Anurag and Hotchkiss, Robert and Potter, Hollis},
  year = {2018},
  month = nov,
  volume = {115},
  pages = {11591--11596},
  publisher = {{National Academy of Sciences}},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.1806905115},
  abstract = {Author Information Robert Lindseya,b,1, Aaron Daluiskia,c, Sumit Chopraa, Alexander Lachapellea,d, Michael Mozera,b, Serge Siculara,e, Douglas Hanela,f, Michael Gardnera,g, Anurag Guptaa,h, Robert Hotchkissa,c, and Hollis Pottera,iaImagen Technologies, New York, NY 10012;bDepartment of Computer Science, University of Colorado, Boulder, CO 80309;cDepartment of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY 10021;dFaculty of Medicine, McGill University, Montreal, QC, Canada, H3A 2R7;eDepartment of Radiology, Mount Sinai Health System, New York, NY 10029;fDepartment of Orthopaedics and Sports Medicine, Harborview Medical Center, University of Washington, Seattle, WA 98104;gDepartment of Orthopaedic Surgery, Stanford University School of Medicine, Stanford, CA 94305;hDepartment of Emergency Medicine, Northwell Health, New Hyde Park, NY 11040;iDepartment of Radiology and Imaging, Hospital for Special Surgery, New York, NY 10021Edited by Terrence J. Sejnowski, Salk Institute for Biological Studies, La Jolla, CA, and approved September 14, 2018 (received for review April 25, 2018) Footnotes{$\carriagereturn$}1To whom correspondence should be addressed. Email: rob@imagen.ai.Author contributions: R.L., A.D., A.L., M.M., and H.P. designed research; R.L., A.L., S.S., D.H., M.G., A.G., and R.H. performed research; R.L. and M.M. analyzed data; and R.L., A.D., S.C., M.M., and H.P. wrote the paper.Conflict of interest statement: The authors are affiliated with Imagen Technologies, a startup company, the eventual products and services of which will be related to the subject matter of the article. The research was funded by Imagen Technologies. The authors own stock options in the company.This article is a PNAS Direct Submission.This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1806905115/-/DCSupplemental.},
  chapter = {Biological Sciences},
  copyright = {Copyright \textcopyright{} 2018 the Author(s). Published by PNAS.. https://creativecommons.org/licenses/by-nc-nd/4.0/This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).},
  file = {/home/work/Zotero/storage/868N7RKU/Lindsey et al_2018_Deep neural network improves fracture detection by clinicians.pdf;/home/work/Zotero/storage/88966UW6/tab-article-info.html},
  journal = {Proceedings of the National Academy of Sciences},
  language = {en},
  number = {45},
  pmid = {30348771}
}

@article{sabaPresentFutureDeep2019,
  title = {The Present and Future of Deep Learning in Radiology},
  author = {Saba, Luca and Biswas, Mainak and Kuppili, Venkatanareshbabu and Cuadrado Godia, Elisa and Suri, Harman S. and Edla, Damodar Reddy and Omerzu, Toma{\v z} and Laird, John R. and Khanna, Narendra N. and Mavrogeni, Sophie and Protogerou, Athanasios and Sfikakis, Petros P. and Viswanathan, Vijay and Kitas, George D. and Nicolaides, Andrew and Gupta, Ajay and Suri, Jasjit S.},
  year = {2019},
  month = may,
  volume = {114},
  pages = {14--24},
  issn = {0720-048X},
  doi = {10.1016/j.ejrad.2019.02.038},
  abstract = {The advent of Deep Learning (DL) is poised to dramatically change the delivery of healthcare in the near future. Not only has DL profoundly affected the healthcare industry it has also influenced global businesses. Within a span of very few years, advances such as self-driving cars, robots performing jobs that are hazardous to human, and chat bots talking with human operators have proved that DL has already made large impact on our lives. The open source nature of DL and decreasing prices of computer hardware will further propel such changes. In healthcare, the potential is immense due to the need to automate the processes and evolve error free paradigms. The sheer quantum of DL publications in healthcare has surpassed other domains growing at a very fast pace, particular in radiology. It is therefore imperative for the radiologists to learn about DL and how it differs from other approaches of Artificial Intelligence (AI). The next generation of radiology will see a significant role of DL and will likely serve as the base for augmented radiology (AR). Better clinical judgement by AR will help in improving the quality of life and help in life saving decisions, while lowering healthcare costs. A comprehensive review of DL as well as its implications upon the healthcare is presented in this review. We had analysed 150 articles of DL in healthcare domain from PubMed, Google Scholar, and IEEE EXPLORE focused in medical imagery only. We have further examined the ethic, moral and legal issues surrounding the use of DL in medical imaging.},
  file = {/home/work/Zotero/storage/R43C35AQ/Saba et al_2019_The present and future of deep learning in radiology.pdf;/home/work/Zotero/storage/BARJXWRL/S0720048X19300919.html},
  journal = {European Journal of Radiology},
  language = {en}
}

@article{sameiAutomatedCharacterizationPerceptual2014,
  title = {Automated Characterization of Perceptual Quality of Clinical Chest Radiographs: {{Validation}} and Calibration to Observer Preference},
  shorttitle = {Automated Characterization of Perceptual Quality of Clinical Chest Radiographs},
  author = {Samei, Ehsan and Lin, Yuan and Choudhury, Kingshuk R. and McAdams, H. Page},
  year = {2014},
  volume = {41},
  pages = {111918},
  issn = {2473-4209},
  doi = {10.1118/1.4899183},
  abstract = {Purpose: The authors previously proposed an image-based technique [Y. Lin et al. Med. Phys. 39, 7019\textendash 7031 (2012)] to assess the perceptual quality of clinical chest radiographs. In this study, an observer study was designed and conducted to validate the output of the program against rankings by expert radiologists and to establish the ranges of the output values that reflect the acceptable image appearance so the program output can be used for image quality optimization and tracking. Methods: Using an IRB-approved protocol, 2500 clinical chest radiographs (PA/AP) were collected from our clinical operation. The images were processed through our perceptual quality assessment program to measure their appearance in terms of ten metrics of perceptual image quality: lung gray level, lung detail, lung noise, rib\textendash lung contrast, rib sharpness, mediastinum detail, mediastinum noise, mediastinum alignment, subdiaphragm\textendash lung contrast, and subdiaphragm area. From the results, for each targeted appearance attribute/metric, 18 images were selected such that the images presented a relatively constant appearance with respect to all metrics except the targeted one. The images were then incorporated into a graphical user interface, which displayed them into three panels of six in a random order. Using a DICOM calibrated diagnostic display workstation and under low ambient lighting conditions, each of five participating attending chest radiologists was tasked to spatially order the images based only on the targeted appearance attribute regardless of the other qualities. Once ordered, the observer also indicated the range of image appearances that he/she considered clinically acceptable. The observer data were analyzed in terms of the correlations between the observer and algorithmic rankings and interobserver variability. An observer-averaged acceptable image appearance was also statistically derived for each quality attribute based on the collected individual acceptable ranges. Results: The observer study indicated that, for each image quality attribute, the averaged observer ranking strongly correlated with the algorithmic ranking (linear correlation coefficient R {$>$} 0.92), with highest correlation (R = 1) for lung gray level and the lowest (R = 0.92) for mediastinum noise. There was a strong concordance between the observers in terms of their rankings (i.e., Kendall's tau agreement {$>$} 0.84). The observers also generally indicated similar tolerance and preference levels in terms of acceptable ranges, as 85\% of the values were close to the overall tolerance or preference levels and the differences were smaller than 0.15. Conclusions: The observer study indicates that the previously proposed technique provides a robust reflection of the perceptual image quality in clinical images. The results established the range of algorithmic outputs for each metric that can be used to quantitatively assess and qualify the appearance quality of clinical chest radiographs.},
  annotation = {\_eprint: https://aapm.onlinelibrary.wiley.com/doi/pdf/10.1118/1.4899183},
  file = {/home/work/Zotero/storage/57LNPRTQ/Samei et al_2014_Automated characterization of perceptual quality of clinical chest radiographs.pdf;/home/work/Zotero/storage/67H9IZYG/1.html},
  journal = {Medical Physics},
  language = {en},
  number = {11}
}

@article{takakiCalculatingTargetExposure2020,
  title = {Calculating the Target Exposure Index Using a Deep Convolutional Neural Network and a Rule Base},
  author = {Takaki, Takeshi and Murakami, Seiichi and Watanabe, Ryo and Aoki, Takatoshi and Fujibuchi, Toshioh},
  year = {2020},
  month = mar,
  volume = {71},
  pages = {108--114},
  issn = {1120-1797},
  doi = {10.1016/j.ejmp.2020.02.012},
  abstract = {Purpose The objective of this study is to determine the quality of chest X-ray images using a deep convolutional neural network (DCNN) and a rule base without performing any visual assessment. A method is proposed for determining the minimum diagnosable exposure index (EI) and the target exposure index (EIt). Methods The proposed method involves transfer learning to assess the lung fields, mediastinum, and spine using GoogLeNet, which is a type of DCNN that has been trained using conventional images. Three detectors were created, and the image quality of local regions was rated. Subsequently, the results were used to determine the overall quality of chest X-ray images using a rule-based technique that was in turn based on expert assessment. The minimum EI required for diagnosis was calculated based on the distribution of the EI values, which were classified as either suitable or non-suitable and then used to ascertain the EIt. Results The accuracy rate using the DCNN and the rule base was 81\%. The minimum EI required for diagnosis was 230, and the EIt was 288. Conclusion The results indicated that the proposed method using the DCNN and the rule base could discriminate different image qualities without any visual assessment; moreover, it could determine both the minimum EI required for diagnosis and the EIt.},
  file = {/home/work/Zotero/storage/8KP87VUR/Takaki et al_2020_Calculating the target exposure index using a deep convolutional neural network.pdf;/home/work/Zotero/storage/PXFSZS6P/S1120179720300466.html},
  journal = {Physica Medica},
  language = {en}
}

@inproceedings{tanEfficientNetRethinkingModel2019,
  title = {{{EfficientNet}}: {{Rethinking Model Scaling}} for {{Convolutional Neural Networks}}},
  shorttitle = {{{EfficientNet}}},
  booktitle = {International {{Conference}} on {{Machine Learning}}},
  author = {Tan, Mingxing and Le, Quoc},
  year = {2019},
  month = may,
  pages = {6105--6114},
  publisher = {{PMLR}},
  issn = {2640-3498},
  abstract = {Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we systematically study model scaling and identify that carefully balancing network depth, width, and resolution can lead to better performance. Based on this observation, we propose a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient. We demonstrate the effectiveness of this method on scaling up MobileNets and ResNet. To go even further, we use neural architecture search to design a new baseline network and scale it up to obtain a family of models, called EfficientNets, which achieve much better accuracy and efficiency than previous ConvNets. In particular, our EfficientNet-B7 achieves state-of-the-art 84.4\% top-1 / 97.1\% top-5 accuracy on ImageNet, while being 8.4x smaller and 6.1x faster on inference than the best existing ConvNet. Our EfficientNets also transfer well and achieve state-of-the-art accuracy on CIFAR-100 (91.7\%), Flowers (98.8\%), and 3 other transfer learning datasets, with an order of magnitude fewer parameters. Source code is at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet.},
  file = {/home/work/Zotero/storage/UUM7H382/Tan_Le_2019_EfficientNet.pdf;/home/work/Zotero/storage/VJTXB5YA/tan19a.html},
  keywords = {Computer Science - Computer Vision and Pattern Recognition,Computer Science - Machine Learning,EfficientNet,read,Statistics - Machine Learning},
  language = {en}
}

@article{thianConvolutionalNeuralNetworks2019,
  title = {Convolutional {{Neural Networks}} for {{Automated Fracture Detection}} and                     {{Localization}} on {{Wrist Radiographs}}},
  author = {Thian, Yee                             Liang and Li, Yiting and Jagmohan, Pooja and Sia, David and Chan, Vincent Ern Yao and Tan, Robby                         T.},
  year = {2019},
  month = jan,
  volume = {1},
  pages = {e180001},
  publisher = {{Radiological Society of North America}},
  doi = {10.1148/ryai.2019180001},
  abstract = {PurposeTo demonstrate the feasibility and performance of an object detection                             convolutional neural network (CNN) for fracture detection and                             localization on wrist radiographs.Materials and MethodsInstitutional review board approval was obtained with waiver of consent                             for this retrospective study. A total of 7356 wrist radiographic studies                             were extracted from a hospital picture archiving and communication                             system. Radiologists annotated all radius and ulna fractures with                             bounding boxes. The dataset was split into training (90\%) and validation                             (10\%) sets and used to train fracture localization models for frontal                             and lateral images. Inception-ResNet Faster R-CNN architecture was                             implemented as a deep learning model. The models were tested on an                             unseen test set of 524 consecutive emergency department wrist                             radiographic studies with two radiologists in consensus as the reference                             standard. Per-fracture, per-image (ie, per-view), and per-study                             sensitivity and specificity were determined. Area under the receiver                             operating characteristic curve (AUC) analysis was performed.ResultsThe model detected and correctly localized 310 (91.2\%) of 340 and 236                             (96.3\%) of 245 of all radius and ulna fractures on the frontal and                             lateral views, respectively. The per-image sensitivity, specificity, and                             AUC were 95.7\% (95\% confidence interval [CI]: 92.4\%, 97.8\%), 82.5\% (95\%                             CI: 77.4\%, 86.8\%), and 0.918 (95\% CI: 0.894, 0.941), respectively, for                             the frontal view and 96.7\% (95\% CI: 93.6\%, 98.6\%), 86.4\% (95\% CI: 81.9\%,                             90.2\%), and 0.933 (95\% CI: 0.912, 0.954), respectively, for the lateral                             view. The per-study sensitivity, specificity, and AUC were 98.1\% (95\%                             CI: 95.6\%, 99.4\%), 72.9\% (95\% CI: 67.1\%, 78.2\%), and 0.895 (95\% CI:                             0.870, 0.920), respectively.ConclusionThe ability of an object detection CNN to detect and localize radius and                             ulna fractures on wrist radiographs with high sensitivity and                             specificity was demonstrated.\textcopyright{} RSNA, 2019},
  file = {/home/work/Zotero/storage/HZM5R543/Thian et al_2019_Convolutional Neural Networks for Automated Fracture Detection and.pdf;/home/work/Zotero/storage/J7T9S3QH/ryai.html},
  journal = {Radiology: Artificial Intelligence},
  number = {1}
}

@article{wangTwostepAutomatedQuality2020,
  title = {A Two-Step Automated Quality Assessment for Liver {{MR}} Images Based on Convolutional Neural Network},
  author = {Wang, Yida and Song, Yang and Wang, Fang and Sun, Jingjing and Gao, Xinyi and Han, Zhe and Shi, Lei and Shao, Guoliang and Fan, Mingxia and Yang, Guang},
  year = {2020},
  month = mar,
  volume = {124},
  publisher = {{Elsevier}},
  issn = {0720-048X, 1872-7727},
  doi = {10.1016/j.ejrad.2020.108822},
  abstract = {{$<$}h2{$>$}Abstract{$<$}/h2{$><$}h3{$>$}Purpose{$<$}/h3{$><$}p{$>$}To propose an automatic approach based on a convolutional neural network (CNN) to evaluate the quality of T2-weighted liver magnetic resonance (MR) images as nondiagnostic (ND) or diagnostic (D).{$<$}/p{$><$}h3{$>$}Materials and methods{$<$}/h3{$><$}p{$>$}We included 150 T2-weighted liver MR imaging examinations in this retrospective study. Each slice of liver image was annotated with a label D or ND by two radiologists with seven and six years of experience, respectively. Additionally, the radiologists segmented the liver region manually as the ground truth for liver segmentation. A CNN was trained to segment the liver region and another CNN was used to classify the qualities of patches extracted from the liver region. The quality of an image was obtained from the percentage of nondiagnostic patches in all liver patches in the image. Treating nondiagnostic as positive, the accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under the receiver operating characteristic curve (AUC), and confusion matrix were used to evaluate our model. A Mann\textendash Whitney \emph{U} test was performed with the statistical significance set at 0.05.{$<$}/p{$><$}h3{$>$}Results{$<$}/h3{$><$}p{$>$}Our model achieved good performance with an accuracy of 88.3 \%, sensitivity of 86.0 \%, specificity of 89.4 \%, PPV of 78.6 \%, NPV of 93.4 \%, and AUC of 0.911 (95 \% confidence interval: 0.882\textendash 0.939, p {$<$} 0.05). The confusion matrix of our model indicated good concordance with that of the radiologists.{$<$}/p{$><$}h3{$>$}Conclusions{$<$}/h3{$><$}p{$>$}The proposed two-step patch-based model achieved excellent performance when assessing the quality of liver MR images.{$<$}/p{$>$}},
  file = {/home/work/Zotero/storage/VPGLVX5V/Wang et al_2020_A two-step automated quality assessment for liver MR images based on.pdf;/home/work/Zotero/storage/WHLTB2NP/fulltext.html},
  journal = {European Journal of Radiology},
  language = {English},
  pmid = {31951895}
}

@inproceedings{wanRegularizationNeuralNetworks2013,
  title = {Regularization of {{Neural Networks}} Using {{DropConnect}}},
  booktitle = {International {{Conference}} on {{Machine Learning}}},
  author = {Wan, Li and Zeiler, Matthew and Zhang, Sixin and Cun, Yann Le and Fergus, Rob},
  year = {2013},
  month = may,
  pages = {1058--1066},
  publisher = {{PMLR}},
  issn = {1938-7228},
  abstract = {We introduce DropConnect, a generalization of DropOut, for regularizing large fully-connected layers within neural networks. When training with Dropout, a randomly selected subset of activations ar...},
  file = {/home/work/Zotero/storage/29AJV436/Wan et al_2013_Regularization of Neural Networks using DropConnect.pdf;/home/work/Zotero/storage/CFT3FCDD/wan13.html},
  language = {en}
}

@article{willisAutomatedQualityControl2018,
  title = {Automated Quality Control Assessment of Clinical Chest Images},
  author = {Willis, Charles E. and Nishino, Thomas K. and Wells, Jered R. and Asher Ai, H. and Wilson, Joshua M. and Samei, Ehsan},
  year = {2018},
  month = oct,
  volume = {45},
  pages = {4377--4391},
  issn = {2473-4209},
  doi = {10.1002/mp.13107},
  abstract = {PURPOSE: The purpose of this study was to determine whether a proposed suite of objective image quality metrics for digital chest radiographs is useful for monitoring image quality in a clinical setting unique from the one where the metrics were developed. METHODS: Seventeen gridless AP chest radiographs from a GE Optima portable digital radiography (DR) unit ("sub-standard" images; Group 2) and 17 digital PA chest radiographs ("standard-of-care" images; Group 1) and 15 gridless (non-routine) PA chest radiographs (images with a gross technical error; Group 3) from a Discovery DR unit were chosen for analysis. Group 2 images were acquired with a lower kVp (100 vs 125) and shorter source-to-image distance (127~cm vs 183~cm) and were expected to have lower quality than Group 1 images. Group 3 images were expected to have degraded contrast vs Group 1 images. Images were anonymized and securely transferred to the Duke University Clinical Imaging Physics Group for analysis using software described and validated previously. Individual image quality was reported in terms of lung gray level, lung detail, lung noise, rib-lung contrast, rib sharpness, mediastinum detail, mediastinum noise, mediastinum alignment, subdiaphragm-lung contrast, and subdiaphragm area. Metrics were compared across groups. To improve precision of means and confidence intervals for routine exams, an additional 66 PA images were acquired, processed, and pooled with Group 1. Three observer studies were conducted to assess whether humans were able to identify images classified by the algorithm as abnormal. RESULTS: Metrics agreed with published Quality Consistency Ranges with three exceptions: higher lung gray level, lower rib-lung contrast, and lower subdiaphragm-lung contrast. Higher (stored) bit depth (14 vs 12) accounted for higher lung gray level values in our images. Values were most internally consistent for Group 1. The most sensitive metric for distinguishing between groups was mediastinum noise, followed closely by lung noise. The least sensitive metrics were mediastinum detail and rib-lung contrast. The algorithm was more sensitive than human observers at detecting suboptimal diagnostic quality images. CONCLUSIONS: The software appears promising for objectively and automatically identifying suboptimal images in a clinical imaging operation. The results can be used to establish local quality consistency ranges and action limits per facility preferences.},
  file = {/home/work/Zotero/storage/CVBNKNXH/Willis et al_2018_Automated quality control assessment of clinical chest images.pdf},
  journal = {Medical Physics},
  language = {eng},
  number = {10},
  pmid = {30053326}
}


