
@InProceedings{mairhoferQualityAssesment,
  title = 	 {An {AI}-based Framework for Diagnostic Quality Assessment of Ankle Radiographs},
  author =       {Mairh{\"o}fer, Dominik and Laufer, Manuel and Simon, Paul Martin and Sieren, Malte and Bischof, Arpad and K{\"a}ster, Thomas and Barth, Erhardt and Barkhausen, J{\"o}rg and Martinetz, Thomas},
  booktitle = 	 {Proceedings of the Fourth Conference on Medical Imaging with Deep Learning},
  pages = 	 {484--496},
  year = 	 {2021},
  editor = 	 {Heinrich, Mattias and Dou, Qi and de Bruijne, Marleen and Lellmann, Jan and Schl\"afer, Alexander and Ernst, Floris},
  volume = 	 {143},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {07--09 Jul},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v143/mairhofer21a/mairhofer21a.pdf},
  url = 	 {https://proceedings.mlr.press/v143/mairhofer21a.html},
  abstract = 	 {The quality of radiographs is of major importance for diagnosis and treatment planning. While most research regarding automated radiograph quality assessment uses technical features such as noise or contrast, we propose to use anatomical structures as more appropriate features. We show that based on such anatomical features, a modular deep-learning framework can serve as a quality control mechanism for the diagnostic quality of ankle radiographs. For evaluation, a dataset consisting of 950 ankle radiographs was collected and their quality was labeled by radiologists. We obtain an average accuracy of 94.1%, which is better than the expert radiologists are on average.}
}

@article{LorensenMCA,
author = {Lorensen, William and Cline, Harvey},
year = {1987},
month = {08},
pages = {163-},
title = {Marching Cubes: A High Resolution 3D Surface Construction Algorithm},
volume = {21},
isbn = {0897912276},
journal = {ACM SIGGRAPH Computer Graphics},
doi = {10.1145/37401.37422}
}
@article{russakoffFastGenerationDigitally2005,
  title = {Fast Generation of Digitally Reconstructed Radiographs Using Attenuation Fields with Application to {{2D-3D}} Image Registration},
  author = {Russakoff, D.B. and Rohlfing, T. and Mori, K. and Rueckert, D. and Ho, A. and Adler, J.R. and Maurer, C.R.},
  year = {2005},
  month = nov,
  journal = {IEEE Transactions on Medical Imaging},
  volume = {24},
  number = {11},
  pages = {1441--1454},
  issn = {1558-254X},
  doi = {10.1109/TMI.2005.856749},
  urldate = {2025-03-04},
  abstract = {Generation of digitally reconstructed radiographs (DRRs) is computationally expensive and is typically the rate-limiting step in the execution time of intensity-based two-dimensional to three-dimensional (2D-3D) registration algorithms. We address this computational issue by extending the technique of light field rendering from the computer graphics community. The extension of light fields, which we call attenuation fields (AFs), allows most of the DRR computation to be performed in a preprocessing step; after this precomputation step, DRRs can be generated substantially faster than with conventional ray casting. We derive expressions for the physical sizes of the two planes of an AF necessary to generate DRRs for a given X-ray camera geometry and all possible object motion within a specified range. Because an AF is a ray-based data structure, it is substantially more memory efficient than a huge table of precomputed DRRs because it eliminates the redundancy of replicated rays. Nonetheless, an AF can require substantial memory, which we address by compressing it using vector quantization. We compare DRRs generated using AFs (AF-DRRs) to those generated using ray casting (RC-DRRs) for a typical C-arm geometry and computed tomography images of several anatomic regions. They are quantitatively very similar: the median peak signal-to-noise ratio of AF-DRRs versus RC-DRRs is greater than 43 dB in all cases. We perform intensity-based 2D-3D registration using AF-DRRs and RC-DRRs and evaluate registration accuracy using gold-standard clinical spine image data from four patients. The registration accuracy and robustness of the two methods is virtually identical whereas the execution speed using AF-DRRs is an order of magnitude faster.},
  keywords = {Application software,Attenuation,Cameras,Casting,Computer graphics,Digitally reconstructed radiographs,Image reconstruction,Image registration,image-guided therapy,intensity-based 2D-3D image registration,light fields,Optical attenuators,Radiography,Rendering (computer graphics)},
}

@article{badalAcceleratingMonteCarlo2009,
  title = {Accelerating {{Monte Carlo}} Simulations of Photon Transport in a Voxelized Geometry Using a Massively Parallel Graphics Processing Unit},
  author = {Badal, Andreu and Badano, Aldo},
  year = {2009},
  journal = {Medical Physics},
  volume = {36},
  number = {11},
  pages = {4878--4880},
  issn = {2473-4209},
  doi = {10.1118/1.3231824},
  urldate = {2025-03-04},
  abstract = {Purpose: It is a known fact that Monte Carlo simulations of radiation transport are computationally intensive and may require long computing times. The authors introduce a new paradigm for the acceleration of Monte Carlo simulations: The use of a graphics processing unit (GPU) as the main computing device instead of a central processing unit (CPU). Methods: A GPU-based Monte Carlo code that simulates photon transport in a voxelized geometry with the accurate physics models from PENELOPE has been developed using the CUDA™ programming model (NVIDIA Corporation, Santa Clara, CA). Results: An outline of the new code and a sample x-ray imaging simulation with an anthropomorphic phantom are presented. A remarkable 27-fold speed up factor was obtained using a GPU compared to a single core CPU. Conclusions: The reported results show that GPUs are currently a good alternative to CPUs for the simulation of radiation transport. Since the performance of GPUs is currently increasing at a faster pace than that of CPUs, the advantages of GPU-based software are likely to be more pronounced in the future.},
  copyright = {{\copyright} 2009 American Association of Physicists in Medicine},
  langid = {english},
  keywords = {Applications,bio-optics,Computer software,Computer-aided diagnosis,CUDA,GPU,Image sensors,including brachytherapy,Ionizing radiation,medical diagnostic computing,Medical physics,Monte Carlo,Monte Carlo methods,PENELOPE,Photons,radiation therapy,Radiation therapy equipment,Radiography,Therapeutic applications,Trajectory models,X-ray imaging},
}


@article{SaitiMultimodalregistration,
author = {Saiti, E. and Theoharis, T.},
title = {Multimodal registration across 3D point clouds and CT-volumes},
year = {2022},
issue_date = {Aug 2022},
publisher = {Pergamon Press, Inc.},
address = {USA},
volume = {106},
number = {C},
issn = {0097-8493},
url = {https://doi.org/10.1016/j.cag.2022.06.012},
doi = {10.1016/j.cag.2022.06.012},
journal = {Comput. Graph.},
month = aug,
pages = {259–266},
numpages = {8},
keywords = {Alignment, 3D point cloud, 3D volume, Data fusion, 3D registration, Multimodal}
}
@article{Grove2010FromCT,
author = {Olya Grove, Khairan Rajab and Les A. Piegl},
title = {From CT to NURBS: Contour Fitting with B-spline Curves},
journal = {Computer-Aided Design and Applications},
volume = {7},
number = {sup1},
pages = {1--19},
year = {2010},
publisher = {Taylor \& Francis},
doi = {10.1080/16864360.2010.10738807},
URL = { 
    
        https://doi.org/10.1080/16864360.2010.10738807
},
eprint = { 
        https://doi.org/10.1080/16864360.2010.10738807
}
}

@inproceedings{unberathDeepDRRCatalystMachine2018,
  title = {{{DeepDRR}} -- {{A Catalyst}} for {{Machine Learning}} in {{Fluoroscopy-Guided Procedures}}},
  booktitle = {Medical {{Image Computing}} and {{Computer Assisted Intervention}} -- {{MICCAI}} 2018},
  author = {Unberath, Mathias and Zaech, Jan-Nico and Lee, Sing Chun and Bier, Bastian and Fotouhi, Javad and Armand, Mehran and Navab, Nassir},
  editor = {Frangi, Alejandro F. and Schnabel, Julia A. and Davatzikos, Christos and {Alberola-L{\'o}pez}, Carlos and Fichtinger, Gabor},
  year = {2018},
  pages = {98--106},
  publisher = {Springer International Publishing},
  address = {Cham},
  doi = {10.1007/978-3-030-00937-3_12},
  abstract = {Machine learning-based approaches outperform competing methods in most disciplines relevant to diagnostic radiology. Interventional radiology, however, has not yet benefited substantially from the advent of deep learning, in particular because of two reasons: (1) Most images acquired during the procedure are never archived and are thus not available for learning, and (2) even if they were available, annotations would be a severe challenge due to the vast amounts of data. When considering fluoroscopy-guided procedures, an interesting alternative to true interventional fluoroscopy is in silico simulation of the procedure from 3D diagnostic CT. In this case, labeling is comparably easy and potentially readily available, yet, the appropriateness of resulting synthetic data is dependent on the forward model. In this work, we propose DeepDRR, a framework for fast and realistic simulation of fluoroscopy and digital radiography from CT scans, tightly integrated with the software platforms native to deep learning. We use machine learning for material decomposition and scatter estimation in 3D and 2D, respectively, combined with analytic forward projection and noise injection to achieve the required performance. On the example of anatomical landmark detection in X-ray images of the pelvis, we demonstrate that machine learning models trained on DeepDRRs generalize to unseen clinically acquired data without the need for re-training or domain adaptation. Our results are promising and promote the establishment of machine learning in fluoroscopy-guided procedures.},
  isbn = {978-3-030-00937-3},
  langid = {english},
  keywords = {Beam hardening,Image-guided procedures,Monte Carlo simulation,Volumetric segmentation},
}

@inproceedings{Chougule2013ConversionsOC,
  author = {Chougule, Vikas and Mulay, Arati and Ahuja, B.},
year = {2013},
month = {12},
pages = {},
title = {Conversions of CT Scan Images into 3D Point Cloud Data for the Development of 3D Solid Model using B-Rep Scheme}
}
@INPROCEEDINGS{keerthiExplorationofGANsForChestXray,
  author={Keerthi, R and Kiran, Kuval and Kiran, Ss and Likitha, P},
  booktitle={2024 IEEE International Conference on Computer Vision and Machine Intelligence (CVMI)}, 
  title={Advancing Medical Imaging: A Comparative Exploration of Generative Adversarial Networks for Chest X-ray Synthesis}, 
  year={2024},
  volume={},
  number={},
  pages={1-7},
  keywords={Training;Measurement;Accuracy;Pneumonia;Machine learning algorithms;Tuberculosis;Generative adversarial networks;Planning;X-ray imaging;Medical diagnostic imaging;Chest X-ray;Generative Adversarial Networks (GANs);Medical Imaging;Lung Infections;Tuberculosis;Deep Learning;Pneumonia;COVID-19;Image Quality Metrics},
  doi={10.1109/CVMI61877.2024.10782852}}

@ARTICLE{liuSynthesisXRaySecurityInspection,
  author={Liu, Jian and Lin, Tim H.},
  journal={IEEE Access}, 
  title={A Framework for the Synthesis of X-Ray Security Inspection Images Based on Generative Adversarial Networks}, 
  year={2023},
  volume={11},
  number={},
  pages={63751-63760},
  keywords={X-ray imaging;Inspection;Generative adversarial networks;Training;Security;Image color analysis;Generators;Image generation;data augmentation;object detection;X-ray security checking;generative adversarial network (GAN)},
  doi={10.1109/ACCESS.2023.3288087}}

@incollection{BADAL2011813,
  title = {Chapter 50 - Fast Simulation of Radiographic Images Using a Monte Carlo x-Ray Transport Algorithm Implemented in {{CUDA}}},
  booktitle = {{{GPU}} Computing Gems Emerald Edition},
  author = {Badal, Andreu and Badano., Aldo},
  editor = {Hwu, Wen-mei W.},
  year = {2011},
  series = {Applications of {{GPU}} Computing Series},
  pages = {813--829},
  publisher = {Morgan Kaufmann},
  address = {Boston},
  doi = {10.1016/B978-0-12-384988-5.00050-4},
  abstract = {Publisher Summary This chapter describes a Monte Carlo algorithm for the simulation of X-ray transport and shows how this algorithm can be efficiently implemented in a Graphics Processing Unit (GPU). The use of X-rays to visualize structures inside the human body has been an essential medical tool for over a century. Imaging systems have to be optimized to provide sufficient image quality for the particular diagnostic tasks, while minimizing the amount of radiation received by the patients. Computer simulations play a significant role in the study of the performance of imaging systems and in the estimation of the radiation doses received by patients. The Monte Carlo (MC) method provides a natural and elegant way to simulate radiation transport in complex systems and is accepted by the community to be the simulation approach in best agreement with experimental measurements. The main reason to develop the software presented in this chapter was the study of clinical imaging systems and, in particular, to create synthetic CT scans, a type of simulation that is too time-consuming to be performed with accurate general-purpose MC transport codes. The code described in this chapter is being actively developed to extend the available features and increase the imaging modalities that can be simulated. The main application of the new simulation code is the evaluation of medical radiographic imaging systems. Example simulations of projection imaging and compute tomography (CT) using a realistic voxelbased human anatomy model are presented.},
  isbn = {978-0-12-384988-5}
}

@inproceedings{salvat2006penelope,
  title={PENELOPE-2006: A Code System for Monte Carlo Simulation of Electron and Photon Transport},
  author={Salvat, Francesc and Fern{\'a}ndez-Varea, Jos{\'e} M and Sempau, Josep},
  booktitle={Workshop Proceedings},
  volume={4},
  pages={7},
  year={2006}
}

@article{lorensenMarchingcubes,
author = {Lorensen, William E. and Cline, Harvey E.},
title = {Marching cubes: A high resolution 3D surface construction algorithm},
year = {1987},
issue_date = {July 1987},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {21},
number = {4},
issn = {0097-8930},
url = {https://doi.org/10.1145/37402.37422},
doi = {10.1145/37402.37422},
abstract = {We present a new algorithm, called marching cubes, that creates triangle models of constant density surfaces from 3D medical data. Using a divide-and-conquer approach to generate inter-slice connectivity, we create a case table that defines triangle topology. The algorithm processes the 3D medical data in scan-line order and calculates triangle vertices using linear interpolation. We find the gradient of the original data, normalize it, and use it as a basis for shading the models. The detail in images produced from the generated surface models is the result of maintaining the inter-slice connectivity, surface data, and gradient information present in the original 3D data. Results from computed tomography (CT), magnetic resonance (MR), and single-photon emission computed tomography (SPECT) illustrate the quality and functionality of marching cubes. We also discuss improvements that decrease processing time and add solid modeling capabilities.},
journal = {SIGGRAPH Comput. Graph.},
month = aug,
pages = {163–169},
numpages = {7}
}

@article{lewinerEfficientMarchingCubes,
author = {Lewiner, Thomas and Lopes, Hélio and Vieira, Antonio and Tavares, Geovan},
year = {2012},
month = {04},
pages = {},
title = {Efficient Implementation of Marching Cubes' Cases with Topological Guarantees},
volume = {8},
journal = {Journal of Graphics Tools},
doi = {10.1080/10867651.2003.10487582}
}
@inproceedings{esterDBSCAN,
author = {Ester, Martin and Kriegel, Hans-Peter and Sander, J\"{o}rg and Xu, Xiaowei},
title = {A density-based algorithm for discovering clusters in large spatial databases with noise},
year = {1996},
publisher = {AAAI Press},
abstract = {Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algorithms offer no solution to the combination of these requirements. In this paper, we present the new clustering algorithm DBSCAN relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN requires only one input parameter and supports the user in determining an appropriate value for it. We performed an experimental evaluation of the effectiveness and efficiency of DBSCAN using synthetic data and real data of the SEQUOIA 2000 benchmark. The results of our experiments demonstrate that (1) DBSCAN is significantly more effective in discovering clusters of arbitrary shape than the well-known algorithm CLAR-ANS, and that (2) DBSCAN outperforms CLARANS by a factor of more than 100 in terms of efficiency.},
booktitle = {Proceedings of the Second International Conference on Knowledge Discovery and Data Mining},
pages = {226–231},
numpages = {6},
keywords = {arbitrary shape of clusters, clustering algorithms, efficiency on large spatial databases, handling nlj4-275oise},
location = {Portland, Oregon},
series = {KDD'96}
}
@article{zhouOpen3D,
    author    = {Qian-Yi Zhou and Jaesik Park and Vladlen Koltun},
    title     = {{Open3D}: {A} Modern Library for {3D} Data Processing},
    journal   = {arXiv:1801.09847},
    year      = {2018},
}
@inproceedings{lauferPatientPoseAssessment2024,
author = {Manuel Laufer and Dominik Mairh{\"o}fer and Malte Sieren and Hauke Gerdes and Fabio Leal dos Reis and Arpad Bischof and Thomas K{\"a}ster and Erhardt Barth and J{\"o}rg Barkhausen and Thomas Martinetz},
title = {{Patient pose assessment in radiography using time-of-flight cameras}},
volume = {12926},
booktitle = {Medical Imaging 2024: Image Processing},
editor = {Olivier Colliot and Jhimli Mitra},
organization = {International Society for Optics and Photonics},
publisher = {SPIE},
pages = {129261J},
keywords = {patient positioning, pose assessment, quality assessment, deep learning, diagnostic quality, radiography, Time-of-Flight cameras, feature fusion},
year = {2024},
doi = {10.1117/12.3000370},
URL = {https://doi.org/10.1117/12.3000370}
}

@software{falconPyTorchLightning2019,
author = {Falcon, William and {The PyTorch Lightning team}},
doi = {10.5281/zenodo.3828935},
license = {Apache-2.0},
month = mar,
title = {{PyTorch Lightning}},
url = {https://github.com/Lightning-AI/lightning},
version = {1.4},
year = {2019}}

@article{alashbanAssessmentImageReject2022,
  title = {An Assessment of Image Reject Rates for Digital Radiography in {{Saudi Arabia}}: {{A}} Cross-Sectional Study},
  shorttitle = {An Assessment of Image Reject Rates for Digital Radiography in {{Saudi Arabia}}},
  author = {Alashban, Yazeed and Shubayr, Nasser and Alghamdi, Abdulrahman A. and Alghamdi, Sami A. and Boughattas, Sami},
  year = {2022},
  month = mar,
  journal = {Journal of Radiation Research and Applied Sciences},
  volume = {15},
  number = {1},
  pages = {219--223},
  issn = {1687-8507},
  doi = {10.1016/j.jrras.2022.01.023},
  urldate = {2023-01-10},
  abstract = {Repeat radiographs are necessary to obtain the required diagnostic information, which increases the patient's radiation dose and detracts from the ``as low as reasonably achievable'' (ALARA) principle. The aim of this study was to analyse the reject rate for digital radiography, causes of repetition, the most commonly repeated types of examinations for fixed and portable radiography and reject rate variations between different technologists. The data were collected from major medical imaging departments in Saudi Arabia (n~=~27,238 x-ray images). Of the 27,238 images acquired, 2,682 were rejected, resulting in an overall reject rate of 8.96\%. Remarkably, when rejected images of individual anatomical areas were evaluated, some areas were observed to have reject rates as high as 20\% and 66\% for fixed and portable digital radiography, respectively. For fixed and portable radiography, pelvic and lumbar spine procedures had the highest reject rates, respectively. Additionally, the projections with the highest reject rates for fixed and portable radiography were the shoulder axial and lumbar spine cross-table lateral projections, respectively. This study recommends initiating more consistent criteria for image quality along with a feedback scheme within the medical imaging departments to guide internal education programs.},
  langid = {english},
  keywords = {Digital radiography,Medical imaging,Reject analysis,Reject rate},
}

@article{atkinsonRejectRateAnalysis2020,
  title = {Reject Rate Analysis in Digital Radiography: An {{Australian}} Emergency Imaging Department Case Study},
  shorttitle = {Reject Rate Analysis in Digital Radiography},
  author = {Atkinson, Samantha and Neep, Michael and Starkey, Deborah},
  year = {2020},
  journal = {Journal of Medical Radiation Sciences},
  volume = {67},
  number = {1},
  pages = {72--79},
  issn = {2051-3909},
  doi = {10.1002/jmrs.343},
  urldate = {2022-12-14},
  abstract = {Introduction Reject analysis in digital radiography (DR) helps guide the education and training of staff, influences department workflow, reduces patient dose and improves department efficiency. The purpose of this study was to investigate rejected radiographs at a major metropolitan emergency imaging department to help form a benchmark of reject rates for DR and to assess what radiographs are being rejected and why. Methods A retrospective longitudinal study was undertaken as an in-depth clinical audit. The data were collected using automated reject analysis software from two digital x-ray systems from June 2015 to April 2017. The overall reject rate, reasons for rejection as well as the reject rates for individual radiographers, examination types and projections were analysed. Results A total of 90,298 radiographic images were acquired and included in the analysis. The average reject rate was 9\%, and the most frequent reasons for image rejection were positioning error (49\%) and anatomy cut-off (21\%). The reject rate varied between radiographers as well as for individual examination types and projections. Conclusions The variation in radiographer reject rates and the high reject rate for some projections indicate that reject analysis is still necessary as a quality assurance tool for DR. A feedback system between radiologists and radiographers may reduce the high percentage of positioning errors by standardising the technical factors used to assess image quality. Future reject analysis should be conducted regularly incorporating an exposure indicator analysis as well as retrospective assessment of individual rejected images.},
  langid = {english},
  keywords = {Computed radiography,digital radiography,reject analysis,reject rate},
}



@inproceedings{bergRobustChestXray2020,
  title = {Robust Chest X-Ray Quality Assessment Using Convolutional Neural Networks and Atlas Regularization},
  booktitle = {Medical {{Imaging}} 2020: {{Image Processing}}},
  author = {von Berg, Jens and Kr{\"o}nke, Sven and Goo{\ss}en, Andr{\'e} and Bystrov, Daniel and Br{\"u}ck, Matthias and Harder, Tim and Wieberneit, Nataly and Young, Stewart},
  year = {2020},
  month = mar,
  volume = {11313},
  pages = {391--398},
  publisher = {{SPIE}},
  doi = {10.1117/12.2549541},
  urldate = {2022-12-14},
  abstract = {The quality of chest radiographs is a practical issue because deviations from quality standards cost radiologists' time, may lead to misdiagnosis and hold legal risks. Automatic and reproducible assessment of the most important quality figures on every acquisition can enable a radiology department to measure, maintain, and improve quality rates on an everyday basis. A method is proposed here to automatically quantify the quality according to the aspects of (i) collimation, (ii) patient rotation, and (iii) inhalation state of a chest PA radiograph by localizing a number of anatomical features and calculating some quality figures in accordance with international standards. The anatomical features related to these quality aspects are robustly detected by a combination of three convolutional neural networks and two probabilistic anatomical atlases. An error analysis demonstrates the accuracy and robustness of the method. The implementation proposed here works in real time (less than a second) on a CPU without any GPU support.}
}

@inproceedings{dengImageNetLargescaleHierarchical2009,
  title = {{{ImageNet}}: {{A}} Large-Scale Hierarchical Image Database},
  shorttitle = {{{ImageNet}}},
  booktitle = {2009 {{IEEE Conference}} on {{Computer Vision}} and {{Pattern Recognition}}},
  author = {Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and {Fei-Fei}, Li},
  year = {2009},
  month = jun,
  pages = {248--255},
  issn = {1063-6919},
  doi = {10.1109/CVPR.2009.5206848},
  abstract = {The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called ``ImageNet'', a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500{\textendash}1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond.},
  keywords = {Explosions,Image databases,Image retrieval,Information retrieval,Internet,Large-scale systems,Multimedia databases,Ontologies,Robustness,Spine},
}

@article{dhillonPositioningErrorsQuality2012,
  title = {Positioning Errors and Quality Assessment in Panoramic Radiography},
  author = {Dhillon, Manu and Raju, Srinivasa M. and Verma, Sankalp and Tomar, Divya and Mohan, Raviprakash S. and Lakhanpal, Manisha and Krishnamoorthy, Bhuvana},
  year = {2012},
  month = dec,
  journal = {Imaging Science in Dentistry},
  volume = {42},
  number = {4},
  pages = {207--212},
  issn = {2233-7822},
  doi = {10.5624/isd.2012.42.4.207},
  urldate = {2022-12-14},
  abstract = {Dhillon M, et al. Imaging Sci Dent. 2012 Dec;42(4):207-212. https://doi.org/10.5624/isd.2012.42.4.207},
  langid = {english},
}

@article{hansenFusingInformationMultiple2019,
  title = {Fusing Information from Multiple {{2D}} Depth Cameras for {{3D}} Human Pose Estimation in the Operating Room},
  author = {Hansen, Lasse and Siebert, Marlin and Diesel, Jasper and Heinrich, Mattias P.},
  year = {2019},
  month = nov,
  journal = {International Journal of Computer Assisted Radiology and Surgery},
  volume = {14},
  number = {11},
  pages = {1871--1879},
  issn = {1861-6429},
  doi = {10.1007/s11548-019-02044-7},
  urldate = {2022-12-14},
  abstract = {For many years, deep convolutional neural networks have achieved state-of-the-art results on a wide variety of computer vision tasks. 3D human pose estimation makes no exception and results on public benchmarks are impressive. However, specialized domains, such as operating rooms, pose additional challenges. Clinical settings include severe occlusions, clutter and difficult lighting conditions. Privacy concerns of patients and staff make it necessary to use unidentifiable data. In this work, we aim to bring robust human pose estimation to the clinical domain.},
  langid = {english},
  keywords = {2D{\textendash}3D information fusion,Convolutional autoencoder,Deep learning,Human pose estimation,Operating room},
}

@article{hofmannImageRejectsGeneral2015,
  title = {Image Rejects in General Direct Digital Radiography},
  author = {Hofmann, Bj{\o}rn and Rosanowsky, Tine Blomberg and Jensen, Camilla and Wah, Kenneth Hong Ching},
  year = {2015},
  month = oct,
  journal = {Acta Radiologica Open},
  volume = {4},
  number = {10},
  pages = {2058460115604339},
  publisher = {{SAGE Publications}},
  issn = {2058-4601},
  doi = {10.1177/2058460115604339},
  urldate = {2023-01-10},
  abstract = {BackgroundThe number of rejected images is an indicator of image quality and unnecessary imaging at a radiology department. Image reject analysis was frequent in the film era, but comparably few and small studies have been published after converting to digital radiography. One reason may be a belief that rejects have been eliminated with digitalization.PurposeTo measure the extension of deleted images in direct digital radiography (DR), in order to assess the rates of rejects and unnecessary imaging and to analyze reasons for deletions, in order to improve the radiological services.Material and MethodsAll exposed images at two direct digital laboratories at a hospital in Norway were reviewed in January 2014. Type of examination, number of exposed images, and number of deleted images were registered. Each deleted image was analyzed separately and the reason for deleting the image was recorded.ResultsOut of 5417 exposed images, 596 were deleted, giving a deletion rate of 11\%. A total of 51.3\% were deleted due to positioning errors and 31.0\% due to error in centering. The examinations with the highest percentage of deleted images were the knee, hip, and ankle, 20.6\%, 18.5\%, and 13.8\% respectively.ConclusionThe reject rate is at least as high as the deletion rate and is comparable with previous film-based imaging systems. The reasons for rejection are quite different in digital systems. This falsifies the hypothesis that digitalization would eliminates rejects. A deleted image does not contribute to diagnostics, and therefore is an unnecessary image. Hence, the high rates of deleted images have implications for management, training, education, as well as for quality.},
  langid = {english},
}

@misc{huangDeepNetworksStochastic2016,
  title = {Deep {{Networks}} with {{Stochastic Depth}}},
  author = {Huang, Gao and Sun, Yu and Liu, Zhuang and Sedra, Daniel and Weinberger, Kilian},
  year = {2016},
  month = jul,
  number = {arXiv:1603.09382},
  eprint = {1603.09382},
  primaryclass = {cs},
  institution = {{arXiv}},
  doi = {10.48550/arXiv.1603.09382},
  urldate = {2022-06-02},
  abstract = {Very deep convolutional networks with hundreds of layers have led to significant reductions in error on competitive benchmarks. Although the unmatched expressiveness of the many layers can be highly desirable at test time, training very deep networks comes with its own set of challenges. The gradients can vanish, the forward flow often diminishes, and the training time can be painfully slow. To address these problems, we propose stochastic depth, a training procedure that enables the seemingly contradictory setup to train short networks and use deep networks at test time. We start with very deep networks but during training, for each mini-batch, randomly drop a subset of layers and bypass them with the identity function. This simple approach complements the recent success of residual networks. It reduces training time substantially and improves the test error significantly on almost all data sets that we used for evaluation. With stochastic depth we can increase the depth of residual networks even beyond 1200 layers and still yield meaningful improvements in test error (4.91\% on CIFAR-10).},
  archiveprefix = {arxiv},
}

@inproceedings{kadkhodamohammadiMultiviewRGBDApproach2017,
  title = {A {{Multi-view RGB-D Approach}} for {{Human Pose Estimation}} in {{Operating Rooms}}},
  booktitle = {2017 {{IEEE Winter Conference}} on {{Applications}} of {{Computer Vision}} ({{WACV}})},
  author = {Kadkhodamohammadi, Abdolrahim and Gangi, Afshin and {de Mathelin}, Michel and Padoy, Nicolas},
  year = {2017},
  month = mar,
  pages = {363--372},
  doi = {10.1109/WACV.2017.47},
  abstract = {Many approaches have been proposed for human pose estimation in single and multi-view RGB images. However, some environments, such as the operating room, are still very challenging for state-of-the-art RGB methods. In this paper, we propose an approach for multi-view 3D human pose estimation from RGB-D images and demonstrate the benefits of using the additional depth channel for pose refinement beyond its use for the generation of improved features. The proposed method permits the joint detection and estimation of the poses without knowing a priori the number of persons present in the scene. We evaluate this approach on a novel multi-view RGB-D dataset acquired during live surgeries and annotated with ground truth 3D poses.},
  keywords = {Detectors,Image color analysis,Pose estimation,Skeleton,Surgery,Three-dimensional displays,Visualization},
}

@misc{kingmaAdamMethodStochastic2017,
  title = {Adam: {{A Method}} for {{Stochastic Optimization}}},
  shorttitle = {Adam},
  author = {Kingma, Diederik P. and Ba, Jimmy},
  year = {2017},
  month = jan,
  number = {arXiv:1412.6980},
  eprint = {1412.6980},
  primaryclass = {cs},
  publisher = {{arXiv}},
  doi = {10.48550/arXiv.1412.6980},
  urldate = {2022-12-15},
  abstract = {We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.},
  archiveprefix = {arxiv},
  keywords = {Computer Science - Machine Learning},
}

@article{kjelleAssessmentImageQuality2022,
  title = {The Assessment of Image Quality and Diagnostic Value in {{X-ray}} Images: A Survey on Radiographers' Reasons for Rejecting Images},
  shorttitle = {The Assessment of Image Quality and Diagnostic Value in {{X-ray}} Images},
  author = {Kjelle, Elin and Chilanga, Catherine},
  year = {2022},
  month = mar,
  journal = {Insights into Imaging},
  volume = {13},
  number = {1},
  pages = {36},
  issn = {1869-4101},
  doi = {10.1186/s13244-022-01169-9},
  urldate = {2023-02-08},
  abstract = {Assessing the quality of diagnostic images is subjective and influenced by factors such education, skills, and experience of the assessor. This study aims to explore the radiographers' assessments of medical usefulness or rejection of X-ray images in specific cases.},
  keywords = {Image quality,Radiography,Radiology,Retake,Survey},
}

@article{kjelleKeepRejectThat2021,
  title = {To Keep or Reject, That Is the Question - {{A}} Survey on Radiologists and Radiographers' Assessments of Plain Radiography Images},
  author = {Kjelle, E. and Schanche, A. K. and Hafskjold, L.},
  year = {2021},
  month = feb,
  journal = {Radiography},
  volume = {27},
  number = {1},
  pages = {115--119},
  issn = {1078-8174},
  doi = {10.1016/j.radi.2020.06.020},
  urldate = {2023-01-10},
  abstract = {Introduction This study aimed to survey radiographers and radiologists' assessment of plain radiographs to identify the imaging clinicians' differences in acceptance of image quality. Method An online, questionnaire was distributed among radiographers (n~=~116) and radiologists (n~=~76) in a hospital trust in Norway, including 30 clinical cases (one image and a short referral text) that were divided into 3 categories; keep, could keep and reject, based on European guidelines. When rejecting, the respondents identified the main reason by ticking a list (positioning, collimation, centering, artifact or exposure error). Group differences were explored using 2-tailed chi-squared test. Inter-subjectivity was measured using Cohen's kappa for multi-rater sample. Results In total, 36\% of the radiographers (n~=~42) and 14\% of the radiologists (n~=~14) responded to the survey. Total response rate was 30\% (56/192). Analysis showed significant difference between radiographers and radiologists in the categories of Reject ({$\chi$}2~=~6.3, df~=~1, p~=~0.01), and Could keep ({$\chi$}2~=~6.3, df~=~1, p~=~0.01), identifying radiologists as keeping more images compared to radiographers. Agreement among radiographers (Cohen's {$\kappa$}: 0,39; 95\% CI: 0.30{\textendash}0.48; p~{$<~$}0.001) and radiologists (Cohen's {$\kappa$}: 0,23; 95\% CI: 0.09{\textendash}0.37; p~{$<~$}0.001) respectively, is fair. The most common reason for rejecting an image is suboptimal positioning. Suboptimal collimation constituted 15\% of the rejected images among radiographers, compared to 5\% among radiologists. Centering, artifacts and exposure error showed quite similar rates as reasons for rejection. Conclusion Radiographers and radiologists seem to agree on the assessment of good quality images, however, radiographers seem more reluctant to accept images of lower quality than radiologists. Implications for practice Further research on reasons for differences in image quality assessment between radiographers and radiologists is needed. This could enable reduction in reject rates and increase image quality in conventional X-ray examinations.},
  langid = {english},
  keywords = {Image quality,Radiographer,Radiologist,Rejection,Retake,Survey},
}

@inproceedings{kopnickEstimationAnklejointSpace2023,
  title = {Estimation of the Ankle-Joint Space Visibility in x-Ray Images Using Convolutional Neural Networks},
  booktitle = {Medical {{Imaging}} 2023: {{Image Processing}}},
  author = {K{\"o}pnick, Johannes and May, Jan Marek and Lundt, Bernd and Br{\"u}ck, Matthias and W{\"u}lker, Christian},
  year = {2023},
  month = apr,
  volume = {12464},
  pages = {61--65},
  publisher = {{SPIE}},
  doi = {10.1117/12.2651757},
  urldate = {2023-11-22},
  abstract = {For the X-ray image acquisition one of the most important factors for diagnostic quality is the patient position with respect to the X-ray tube and the detector. In case of orthopedic lateral ankle examinations, inaccurate positioning might lead to a covered joint space. This could make a reliable reading of the images impossible, which necessitates a retake. The presented approach estimates the joint space visibility of lateral ankle X-ray images. An annotation method for the joint space visibility is proposed which depends on the condyle alignment of the talus. A Convolutional Neural Network (CNN) was trained to estimate the joint space visibility. Additionally, the plausibility of the approach was confirmed by an experimental phantom setup. The estimations on a clinical dataset show that using the quality measure in regression space results in a sensitivity of 0.85 and a specificity of 0.91 for a clinically reasonable definition of image quality.},
}

@inproceedings{kronkeCNNbasedPoseEstimation2022,
  title = {{{CNN-based}} Pose Estimation for Assessing Quality of Ankle-Joint {{X-ray}} Images},
  booktitle = {Medical {{Imaging}} 2022: {{Image Processing}}},
  author = {Kr{\"o}nke, Sven and von Berg, Jens and Brueck, Matthias and Bystrov, Daniel and Goo{\ss}en, Andr{\'e} and Harder, Tim and Lundt, Bernd and May, Jan Marek and Wieberneit, Nataly and Wissel, Tobias and Hertgers, Omar and Lamb, Hildo J. and Young, Stewart},
  year = {2022},
  month = apr,
  volume = {12032},
  pages = {344--352},
  publisher = {{SPIE}},
  doi = {10.1117/12.2611734},
  urldate = {2022-04-20},
  abstract = {A novel technology for estimating both the pose and the joint flexion from a single musculoskeletal X-ray image is presented for automatic quality assessment of patient positioning. The method is based on convolutional neural networks and does not require pose or flexion labels of the X-ray images for the training phase. The task is split into two steps: (i) detection of relevant bone contours in the X-ray by a feature-detection network and (ii) regression of the pose and flexion parameters by a pose-estimation network based upon the detected contours. This separation enables the pose-estimation network to be trained using synthetic contours, which are generated via projections of an articulated 3D model of the target anatomy. It is demonstrated that the use of data-augmentation techniques during training of the pose-estimation network significantly contributes to the robustness of the algorithm. Feasibility of the approach is illustrated using lateral ankle X-ray exams. Validation was performed using X-rays of an anthropomorphic phantom of the foot-ankle joint, imaged in various controlled positions. Reference pose parameters were established by an expert using an interactive tool to align the articulated 3D joint model with the phantom image. Errors in pose estimation are in the range of 2 degrees per pose angle and at the level of the expert performance. Using the rigid foot phantom the flexion parameter was constant, but the overall results indicate accurate estimation also of this parameter.},
}

@article{littleUnifiedDatabaseRejected2017,
  title = {Unified {{Database}} for {{Rejected Image Analysis Across Multiple Vendors}} in {{Radiography}}},
  author = {Little, Kevin J. and Reiser, Ingrid and Liu, Lili and Kinsey, Tiffany and S{\'a}nchez, Adrian A. and Haas, Kateland and Mallory, Florence and Froman, Carmen and Lu, Zheng Feng},
  year = {2017},
  month = feb,
  journal = {Journal of the American College of Radiology},
  volume = {14},
  number = {2},
  pages = {208--216},
  issn = {1546-1440},
  doi = {10.1016/j.jacr.2016.07.011},
  urldate = {2022-12-14},
  abstract = {Reject rate analysis has been part of radiography departments' quality control since the days of screen-film radiography. In the era of digital radiography, one might expect that reject rate analysis is easily facilitated because of readily available information produced by the modality during the examination procedure. Unfortunately, this is not always the case. The lack of an industry standard and the wide variety of system log entries and formats have made it difficult to implement a robust multivendor reject analysis program, and logs do not always include all relevant information. The increased use of digital detectors exacerbates this problem because of higher reject rates associated with digital radiography compared with computed radiography. In this article, the authors report on the development of a unified database for vendor-neutral reject analysis across multiple sites within an academic institution and share their experience from a team-based approach to reduce reject rates.},
  langid = {english},
  keywords = {database,dose reduction,radiography,Reject rate,repeat rate},
}

@inproceedings{mairhoferAIbasedFrameworkDiagnostic2021,
  title = {An {{AI-based Framework}} for {{Diagnostic Quality Assessment}} of {{Ankle Radiographs}}},
  booktitle = {Proceedings of the {{Fourth Conference}} on {{Medical Imaging}} with {{Deep Learning}}},
  author = {Mairh{\"o}fer, Dominik and Laufer, Manuel and Simon, Paul Martin and Sieren, Malte and Bischof, Arpad and K{\"a}ster, Thomas and Barth, Erhardt and Barkhausen, J{\"o}rg and Martinetz, Thomas},
  year = {2021},
  month = aug,
  pages = {484--496},
  publisher = {{PMLR}},
  issn = {2640-3498},
  urldate = {2022-01-26},
  abstract = {The quality of radiographs is of major importance for diagnosis and treatment planning. While most research regarding automated radiograph quality assessment uses technical features such as noise or contrast, we propose to use anatomical structures as more appropriate features. We show that based on such anatomical features, a modular deep-learning framework can serve as a quality control mechanism for the diagnostic quality of ankle radiographs. For evaluation, a dataset consisting of 950 ankle radiographs was collected and their quality was labeled by radiologists. We obtain an average accuracy of 94.1\%, which is better than the expert radiologists are on average.},
  langid = {english},
}

@article{marin-jimenez3DHumanPose2018,
  title = {{{3D}} Human Pose Estimation from Depth Maps Using a Deep Combination of Poses},
  author = {{Mar{\'i}n-Jim{\'e}nez}, Manuel J. and {Romero-Ramirez}, Francisco J. and {Mu{\~n}oz-Salinas}, Rafael and {Medina-Carnicer}, Rafael},
  year = {2018},
  month = aug,
  journal = {Journal of Visual Communication and Image Representation},
  volume = {55},
  pages = {627--639},
  issn = {1047-3203},
  doi = {10.1016/j.jvcir.2018.07.010},
  urldate = {2023-02-08},
  abstract = {Many real-world applications require the estimation of human body joints for higher-level tasks as, for example, human behaviour understanding. In recent years, depth sensors have become a popular approach to obtain three-dimensional information. The depth maps generated by these sensors provide information that can be employed to disambiguate the poses observed in two-dimensional images. This work addresses the problem of 3D human pose estimation from depth maps employing a Deep Learning approach. We propose a model, named Deep Depth Pose (DDP), which receives a depth map containing a person and a set of predefined 3D prototype poses and returns the 3D position of the body joints of the person. In particular, DDP is defined as a ConvNet that computes the specific weights needed to linearly combine the prototypes for the given input. We have thoroughly evaluated DDP on the challenging `ITOP' and `UBC3V' datasets, which respectively depict realistic and synthetic samples, defining a new state-of-the-art on them.},
  langid = {english},
  keywords = {3D human pose,Body limbs,ConvNets,Depth maps},
}

@article{mengAutomatedQualityAssessment2022,
  title = {Automated Quality Assessment of Chest Radiographs Based on Deep Learning and Linear Regression Cascade Algorithms},
  author = {Meng, Yu and Ruan, Jingru and Yang, Bailin and Gao, Yang and Jin, Jianqiu and Dong, Fangfang and Ji, Hongli and He, Linyang and Cheng, Guohua and Gong, Xiangyang},
  year = {2022},
  month = nov,
  journal = {European Radiology},
  volume = {32},
  number = {11},
  pages = {7680--7690},
  issn = {1432-1084},
  doi = {10.1007/s00330-022-08771-x},
  urldate = {2022-12-14},
  abstract = {Develop and evaluate the performance of deep learning and linear regression cascade algorithms for automated assessment of the image layout and position of chest radiographs.},
  langid = {english},
  keywords = {Chest X-ray,Deep learning,Linear models,Quality control},
}

@incollection{paszkePyTorchImperativeStyle2019,
  title = {{{PyTorch}}: An Imperative Style, High-Performance Deep Learning Library},
  shorttitle = {{{PyTorch}}},
  booktitle = {Proceedings of the 33rd {{International Conference}} on {{Neural Information Processing Systems}}},
  author = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and K{\"o}pf, Andreas and Yang, Edward and DeVito, Zach and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith},
  year = {2019},
  month = dec,
  number = {721},
  pages = {8026--8037},
  publisher = {{Curran Associates Inc.}},
  address = {{Red Hook, NY, USA}},
  urldate = {2023-01-13},
  abstract = {Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it provides an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several common benchmarks.},
}

@article{plachtFastTimeofflightCamera2012,
  title = {Fast Time-of-Flight Camera Based Surface Registration for Radiotherapy Patient Positioning},
  author = {Placht, Simon and Stancanello, Joseph and Schaller, Christian and Balda, Michael and Angelopoulou, Elli},
  year = {2012},
  journal = {Medical Physics},
  volume = {39},
  number = {1},
  pages = {4--17},
  issn = {2473-4209},
  doi = {10.1118/1.3664006},
  urldate = {2022-12-15},
  abstract = {Purpose: This work introduces a rigid registration framework for patient positioning in radiotherapy, based on real-time surface acquisition by a time-of-flight (ToF) camera. Dynamic properties of the system are also investigated for future gating/tracking strategies. Methods: A novel preregistration algorithm, based on translation and rotation-invariant features representing surface structures, was developed. Using these features, corresponding three-dimensional points were computed in order to determine initial registration parameters. These parameters became a robust input to an accelerated version of the iterative closest point (ICP) algorithm for the fine-tuning of the registration result. Distance calibration and Kalman filtering were used to compensate for ToF-camera dependent noise. Additionally, the advantage of using the feature based preregistration over an ``ICP only'' strategy was evaluated, as well as the robustness of the rigid-transformation-based method to deformation. Results: The proposed surface registration method was validated using phantom data. A mean target registration error (TRE) for translations and rotations of 1.62 {$\pm$} 1.08 mm and 0.07{\textdegree} {$\pm$} 0.05{\textdegree}, respectively, was achieved. There was a temporal delay of about 65 ms in the registration output, which can be seen as negligible considering the dynamics of biological systems. Feature based preregistration allowed for accurate and robust registrations even at very large initial displacements. Deformations affected the accuracy of the results, necessitating particular care in cases of deformed surfaces. Conclusions: The proposed solution is able to solve surface registration problems with an accuracy suitable for radiotherapy cases where external surfaces offer primary or complementary information to patient positioning. The system shows promising dynamic properties for its use in gating/tracking applications. The overall system is competitive with commonly-used surface registration technologies. Its main benefit is the usage of a cost-effective off-the-shelf technology for surface acquisition. Further strategies to improve the registration accuracy are under development.},
  langid = {english},
  keywords = {Accessories therefor,Calibrating of instruments or apparatus,calibration,Calibration,cameras,Cameras,Computed tomography,deformation,Details of cameras or camera bodies,Digital computing or data processing equipment or methods,Dosimetry,Image data processing or generation,image registration,in general,including brachytherapy,Inductively coupled plasma,Interpolation,iterative methods,Kalman filtering,Kalman filters,medical image processing,Medical imaging,noise,Numerical approximation and analysis,phantoms,Pneumodynamics,radiation therapy,Radiation therapy,radiotherapy,registration,Registration,specially adapted for specific applications,Standards and calibration,Surface dynamics,surface features,surface structure,Testing or calibrating of apparatus or arrangements provided for in groups G01D1/00 to G01D15/00,Therapeutic applications,time-of-flight cameras},
}

@article{seelandMultiviewClassificationConvolutional2021a,
  title = {Multi-View Classification with Convolutional Neural Networks},
  author = {Seeland, Marco and M{\"a}der, Patrick},
  year = {2021},
  month = jan,
  journal = {PLOS ONE},
  volume = {16},
  number = {1},
  pages = {e0245230},
  publisher = {{Public Library of Science}},
  issn = {1932-6203},
  doi = {10.1371/journal.pone.0245230},
  urldate = {2022-12-15},
  abstract = {Humans' decision making process often relies on utilizing visual information from different views or perspectives. However, in machine-learning-based image classification we typically infer an object's class from just a single image showing an object. Especially for challenging classification problems, the visual information conveyed by a single image may be insufficient for an accurate decision. We propose a classification scheme that relies on fusing visual information captured through images depicting the same object from multiple perspectives. Convolutional neural networks are used to extract and encode visual features from the multiple views and we propose strategies for fusing these information. More specifically, we investigate the following three strategies: (1) fusing convolutional feature maps at differing network depths; (2) fusion of bottleneck latent representations prior to classification; and (3) score fusion. We systematically evaluate these strategies on three datasets from different domains. Our findings emphasize the benefit of integrating information fusion into the network rather than performing it by post-processing of classification scores. Furthermore, we demonstrate through a case study that already trained networks can be easily extended by the best fusion strategy, outperforming other approaches by large margin.},
  langid = {english},
  keywords = {Ants,Convolution,Crops,Flowering plants,Leaves,Neural networks,Plants,Vision},
}

@article{srivastavaDropoutSimpleWay2014,
  title = {Dropout: {{A Simple Way}} to {{Prevent Neural Networks}} from {{Overfitting}}},
  shorttitle = {Dropout},
  author = {Srivastava, Nitish and Hinton, Geoffrey and Krizhevsky, Alex and Sutskever, Ilya and Salakhutdinov, Ruslan},
  year = {2014},
  journal = {Journal of Machine Learning Research},
  volume = {15},
  number = {56},
  pages = {1929--1958},
  issn = {1533-7928},
  urldate = {2023-01-14},
  abstract = {Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making it difficult to deal with overfitting by combining the predictions of many different large neural nets at test time. Dropout is a technique for addressing this problem. The key idea is to randomly drop units (along with their connections) from the neural network during training. This prevents units from co-adapting too much. During training, dropout samples from an exponential number of different {\^a}thinned{\^a} networks. At test time, it is easy to approximate the effect of averaging the predictions of all these thinned networks by simply using a single unthinned network that has smaller weights. This significantly reduces overfitting and gives major improvements over other regularization methods. We show that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets.},
}

@misc{srivastavMVORMultiviewRGBD2021,
  title = {{{MVOR}}: {{A Multi-view RGB-D Operating Room Dataset}} for {{2D}} and {{3D Human Pose Estimation}}},
  shorttitle = {{{MVOR}}},
  author = {Srivastav, Vinkle and Issenhuth, Thibaut and Kadkhodamohammadi, Abdolrahim and {de Mathelin}, Michel and Gangi, Afshin and Padoy, Nicolas},
  year = {2021},
  month = aug,
  number = {arXiv:1808.08180},
  eprint = {1808.08180},
  primaryclass = {cs},
  publisher = {{arXiv}},
  doi = {10.48550/arXiv.1808.08180},
  urldate = {2022-12-14},
  abstract = {Person detection and pose estimation is a key requirement to develop intelligent context-aware assistance systems. To foster the development of human pose estimation methods and their applications in the Operating Room (OR), we release the Multi-View Operating Room (MVOR) dataset, the first public dataset recorded during real clinical interventions. It consists of 732 synchronized multi-view frames recorded by three RGB-D cameras in a hybrid OR. It also includes the visual challenges present in such environments, such as occlusions and clutter. We provide camera calibration parameters, color and depth frames, human bounding boxes, and 2D/3D pose annotations. In this paper, we present the dataset, its annotations, as well as baseline results from several recent person detection and 2D/3D pose estimation methods. Since we need to blur some parts of the images to hide identity and nudity in the released dataset, we also present a comparative study of how the baselines have been impacted by the blurring. Results show a large margin for improvement and suggest that the MVOR dataset can be useful to compare the performance of the different methods.},
  archiveprefix = {arxiv},
  keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@article{stephenson-smithDigitalRadiographyReject2021,
  title = {Digital Radiography Reject Analysis of Examinations with Multiple Rejects: An {{Australian}} Emergency Imaging Department Clinical Audit},
  shorttitle = {Digital Radiography Reject Analysis of Examinations with Multiple Rejects},
  author = {{Stephenson-Smith}, Brittany and Neep, Michael J and Rowntree, Pamela},
  year = {2021},
  journal = {Journal of Medical Radiation Sciences},
  volume = {68},
  number = {3},
  pages = {245--252},
  issn = {2051-3909},
  doi = {10.1002/jmrs.468},
  urldate = {2022-12-14},
  abstract = {Introduction The largest source of manmade ionising radiation exposure to the public stems from diagnostic medical imaging examinations. Reject analysis, a form of quality assurance, was introduced to minimise repeat exposures. The purpose of this study was to analyse projection-specific reject rates and radiographic examinations with multiple rejects. Methods A retrospective audit of rejected radiographs was undertaken in a busy Australian metropolitan emergency digital X-ray room from March to June 2018. The data were collected by reject analysis software embedded within the X-ray unit. Reject rates, and reasons for rejection for each X-ray projection were analysed. Results Data from 11, 596 images showed overall reject rate was 10.3\% and the overall multiple reject rate was 1.3\%. The projections with both a high number and high percentage of rejects were antero-posterior (AP) chest (175, 18.1\%), AP pelvis (78, 22.5\%), horizontal beam hip (61, 33.5\%) and horizontal beam knee (116, 30.5\%). The projections with both a high frequency and multiple reject rate were horizontal beam knee (32, 8.4\%) and horizontal beam hip (17, 9.3\%). The top reasons for multiple rejects were positioning (67.1\%) and anatomy cut-off (8.4\%). Conclusions The findings of this study demonstrated that projection-specific reject and multiple reject analysis in digital radiography is necessary in identifying areas for quality improvement which will reduce radiation exposure to patients. Projections that were frequently repeated in this study were horizontal beam knee and horizontal beam hip. Future research could involve re-auditing the department following the implementation of improvement strategies to reduce unnecessary radiation exposure.},
  langid = {english},
  keywords = {digital radiography,multiple repeats,reject analysis,reject rate,X-rays},
}

@article{sujarInteractiveTeachingEnvironment2022,
  title = {Interactive Teaching Environment for Diagnostic Radiography with Real-Time {{X-ray}} Simulation and Patient Positioning},
  author = {Sujar, Aaron and Kelly, Graham and Garc{\'i}a, Marcos and Vidal, Franck P.},
  year = {2022},
  month = jan,
  journal = {International Journal of Computer Assisted Radiology and Surgery},
  volume = {17},
  number = {1},
  pages = {85--95},
  issn = {1861-6429},
  doi = {10.1007/s11548-021-02499-7},
  urldate = {2022-12-14},
  abstract = {Traditional undergraduate radiographer training mixes academic lectures and clinical practice. Our goal is to bridge the current disconnection between theory and practice in a safe environment, avoiding the risk of radiation for both practitioners and patients.  To this end, this research proposes a new software to teach diagnostic radiography using real-time interactive X-ray simulation and patient positioning.},
  langid = {english},
  keywords = {Diagnostic radiography,Medical diagnostic imaging,Medical simulation,Training,Virtual reality,X-rays},
}

@article{takamotoDevelopmentClinicalApplication2020,
  title = {Development and {{Clinical Application}} of a {{Novel Non-contact Early Airflow Limitation Screening System Using}} an {{Infrared Time-of-Flight Depth Image Sensor}}},
  author = {Takamoto, Hiroki and Nishine, Hiroki and Sato, Shohei and Sun, Guanghao and Watanabe, Sadao and Seokjin, Kim and Asai, Masahito and Mineshita, Masamichi and Matsui, Takemi},
  year = {2020},
  journal = {Frontiers in Physiology},
  volume = {11},
  issn = {1664-042X},
  urldate = {2022-12-14},
  abstract = {Obstructive pulmonary diseases, such as diffuse panbronchiolitis (DPB), asthma, chronic obstructive pulmonary disease (COPD), and asthma COPD overlap syndrome (ACOS) trigger a severe reaction at some situations. Detecting early airflow limitation caused by diseases above is critical to stop the progression. Thus, there is a need for tools to enable self-screening of early airflow limitation at home. Here, we developed a novel non-contact early airflow limitation screening system (EAFL-SS) that does not require calibration to the individual by a spirometer. The system is based on an infrared time-of-flight (ToF) depth image sensor, which is integrated into several smartphones for photography focusing or augmented reality. The EAFL-SS comprised an 850 nm infrared ToF depth image sensor (224 {\texttimes} 171 pixels) and custom-built data processing algorithms to visualize anterior-thorax three-dimensional motions in real-time. Multiple linear regression analysis was used to determine the amount of air compulsorily exhaled after maximal inspiration (referred to as the forced vital capacity, FVCEAFL{\textendash}SS) from the ToF-derived anterior-thorax forced vital capacity (FVC), height, and body mass index as explanatory variables and spirometer-derived FVC as the objective variable. The non-contact measurement is automatically started when an examinee is sitting 35 cm away from the EAFL-SS. A clinical test was conducted with 32 COPD patients (27/5 M/F, 67{\textendash}93 years) as typical airflow limitation cases recruited at St. Marianna University Hospital and 21 healthy volunteers (10/11 M/F, 23{\textendash}79 years). The EAFL-SS was used to monitor the respiration of examinees during forced exhalation while sitting still, and a spirometer was used simultaneously as a reference. The forced expiratory volume in 1 s (FEV1\%EAFL{\textendash}SS) was evaluated as a percentage of the FVCEAFL{\textendash}SS, where values less than 70\% indicated suspected airflow limitation. Leave-one-out cross-validation analysis revealed that this system provided 81\% sensitivity and 90\% specificity. Further, the FEV1EAFL{\textendash}SS values were closely correlated with that measured using a spirometer (r = 0.85, p {$<$} 0.0001). Hence, EAFL-SS appears promising for early airflow limitation screening at home.},
}

@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},
  urldate = {2021-02-04},
  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.},
  langid = {english},
  keywords = {Computer Science - Computer Vision and Pattern Recognition,Computer Science - Machine Learning,EfficientNet,read,Statistics - Machine Learning},
}
