Table of Contents

• Varying performance levels for diagnosing mammographic images depending on reader nationality have AI and educational implicationsXuetong Tao, Ziba Gandomkar, Tong Li, Warren M. Reed, Patrick C. Brennan. [doi]
• Assessment of a new CAD-generated imaging marker to predict risk of having mammography-occult tumorsSeyedehnafiseh Mirniaharikandehei, Alan B. Hollingsworth, Meredith A. Jones, Hong Liu 0003, Yuchen Qiu, Bin Zheng 0001. [doi]
• Investigation of adversarial robust training for establishing interpretable CNN-based numerical observersSourya Sengupta, Craig K. Abbey, Kaiyan Li 0002, Mark A. Anastasio. [doi]
• Investigating reading strategies and eye behaviours associated with high diagnostic performance when reading digital breast tomosynthesis (DBT) imagesGeorge Partridge, Peter Phillips, Iain T. Darker, Yan Chen 0012. [doi]
• High volume chest radiography to facilitate pulmonary nodule identification on chest radiographsTheresa X. Pham, Grace G. Zhu, Soham Banerjee, William F. Auffermann. [doi]
• Sequestration of imaging studies in MIDRC: a multi-institutional data commonsNatalie M. Baughan, Heather M. Whitney, Karen Drukker, Berkman Sahiner, Tingting Hu, Hyun J. Grace Kim, Michael F. McNitt-Gray, Kyle J. Myers, Maryellen L. Giger. [doi]
• Predicting human detection performance in magnetic resonance imaging (MRI) with total variation and wavelet sparsity regularizersAlexandra G. O'Neill, Sajan Goud Lingala, Angel R. Pineda. [doi]
• Is this good enough? On expert perception of brain tumor segmentation qualityKatharina Hoebel, Christopher P. Bridge, Sara Ahmed, Oluwatosin Akintola, Caroline Chung, Raymond Y. Huang, Jason Johnson, Albert Kim, K. Ina Ly, Ken Chang, Jay B. Patel, Marco Pinho, Tracy Batchelor, Bruce R. Rosen, Elizabeth R. Gerstner, Jayashree Kalpathy-Cramer. [doi]
• Visibility of noise texture changes in CT imagesLuuk J. Oostveen, Kirsten Boedeker, Daniel W. Shin, Craig K. Abbey, Ioannis Sechopoulos. [doi]
• A deep Q-learning method for optimizing visual search strategies in backgrounds of dynamic noiseWeimin Zhou, Miguel P. Eckstein. [doi]
• Diagnostic efficacy in screening mammograms does not improve with peer reading strategy: a Sino-Australian studyWing Lam Chiu, Tong Li, Sarah J. Lewis 0001. [doi]
• No-gold-standard evaluation of quantitative imaging methods in the presence of correlated noiseZiping Liu, Zekun Li, Joyce Mhlanga, Barry A. Siegel, Abhinav K. Jha. [doi]
• Analyzing neural networks applied to an anatomical simulation of the breastCraig K. Abbey, Sourya Sengupta, Weimin Zhou, Andreu Badal, Rongping Zeng, Frank W. Samuelson, Miguel P. Eckstein, Kyle J. Myers, Mark A. Anastasio, Jovan G. Brankov. [doi]
• Visual grading characteristic (VGC) analysis of uterine artery embolisation (UAE) image quality assessment by interventional radiologists and interventional radiographersDon J. Nocum, John W. Robinson, Mark Halaki, Magnus Båth, Nejc Mekis, Eisen Liang, Nadine Thompson, Michelle Moscova, Warren M. Reed. [doi]
• Identical-test Roe and Metz simulation model for validating multi-reader methods of analysis for comparing different radiologic imaging modalitiesStephen L. Hillis. [doi]
• In between are the doors of perceptionElizabeth A. Krupinski. [doi]
• Performance evaluation of image quality metrics for perceptual assessment of low-dose computed tomography imagesWonkyeong Lee, Eunbyeol Cho, Wonjin Kim, Jang-Hwan Choi 0001. [doi]
• The reliability of radiologists' first impression interpreting a screening mammogramZiba Gandomkar, Somphone Siviengphanom, Moayyad E. Suleiman, Dennis Wong, Warren M. Reed, Ernest U. Ekpo, Dong Xu, Sarah J. Lewis 0001, Patrick C. Brennan. [doi]
• MATLAB toolbox for ROC analysis of multi-reader multi-case diagnostic imaging studiesBrian J. Smith, Stephen L. Hillis. [doi]
• Camera-based distance detection and contact tracing to monitor potential spread of COVID-19Ming Li 0052, Nicole Varble, Baris Turkbey, Sheng Xu 0001, Bradford J. Wood. [doi]
• Using reader disagreement index as a tool for monitoring impact on read quality due to reader fatigue in central reviewersManish Sharma, Madhuri Madasu, Sree Sudha Kota, Surabhi Bajpai, Yibin Shao, Srinivas Pasupuleti, Michael O'Connor. [doi]
• Noise2Quality: non-reference, pixel-wise assessment of low dose CT image qualityAyaan Haque, Adam S. Wang, Abdullah-Al-Zubaer Imran. [doi]
• Influence of deep learning reconstruction on task-based model observer performance in CT: an anthropomorphic head phantom studyIrene Hernández-Girón, Touko Kaasalainen, Teemu Mäkelä, Juha I. Peltonen, Mika Kortesniemi. [doi]
• Diagnostic performances of radiology trainees in reading digital breast tomosynthesis and the synthesized viewPhuong Dung (Yun) Trieu, Jennifer Noakes, Natacha Borecky, Tong Li, Patrick C. Brennan, Melissa L. Barron, Sarah J. Lewis 0001. [doi]
• Investigating the limited performance of a deep-learning-based SPECT denoising approach: an observer-study-based characterizationZitong Yu, Md. Ashequr Rahman, Abhinav K. Jha. [doi]
• Comparison of performance in breast lesions classification using radiomics and deep transfer learning: an assessment studyGopichandh Danala, Sai Kiran Reddy Maryada, Huong Pham, Warid Islam, Meredith A. Jones, Bin Zheng 0001. [doi]
• Breast density distribution among the Saudi screening population and correlation between radiologist visual assessment and two automated methodsAreej S. Aloufi, Abdulrahman AlNaeem, Abeer Almousa, Mehreen Malik, Amani Hashem, Fatina Altahan, Mahmoud Elsharkawi, Manal ElMahdy, Reham Altokhais, Sara Alsultan, Rasha Sahloul, Khaled Alzimami, Steven Squires, Elaine F. Harkness, Susan M. Astley. [doi]
• Test-set training is linked to increased breast screening cancer detection ratesBasel A. Qenam, Tong Li, Patrick C. Brennan. [doi]
• Learned Hotelling observers for use with multi-modal dataJason L. Granstedt, Fu Li, Umberto Villa, Mark A. Anastasio. [doi]
• Interpretable deep learning models for better clinician-AI communication in clinical mammographyAlina Jade Barnett, Vaibhav Sharma, Neel Gajjar, Jerry Fang, Fides Schwartz, Chaofan Chen, Joseph Y. Lo, Cynthia Rudin. [doi]
• Assessment of manual and automated intracranial artery diameter measurementsIris N. Vos, Maud E. H. Ophelders, Ynte M. Ruigrok, Birgitta K. Velthuis, Hugo J. Kuijf. [doi]
• Experience does not protect against missed incidental-findings in radiologyLauren H. Williams, Megan Mills, Ann J. Carrigan, Anina N. Rich, Trafton Drew. [doi]
• AI-based analysis of radiologist's eye movements for fatigue estimation: a pilot study on chest X-raysIlya Pershin, Maksim Kholiavchenko, Bulat Maksudov, Tamerlan Mustafaev, Bulat Ibragimov. [doi]
• Comparison of deep learning architectures for COVID-19 diagnosis using chest X-ray imagesDenilson Sampén, Roberto J. Lavarello. [doi]
• Wait-time-saving analysis and clinical effectiveness of computer-aided triage and notification (CADt) devices based on queueing theoryYee Lam Elim Thompson, Gary Levine, Weijie Chen, Berkman Sahiner, Qin Li 0018, Nicholas Petrick, Frank W. Samuelson. [doi]
• Satisfaction of search (SOS) error and new lesions identification on imaging in central review for clinical trialsManish Sharma, Sree Sudha Kota, Madhuri Madasu, Surabhi Bajpai, Yibin Shao, Srinivas Pasupuleti, Michael O'Connor. [doi]
• Using virtual clinical trials to determine the accuracy of AI-based quantitative imaging biomarkers in oncology trials using standard-of-care CTDarrin W. Byrd, Dennis Bontempi, Hao Yang, Hugo J. W. L. Aerts, Binzhang Zhao, Andrey Fedorov, Lawrence H. Schwartz, Tavis Allison, Chaya Moscowitz, Paul E. Kinahan. [doi]
• Case-based repeatability and operating point variability of AI: breast lesion classification based on deep transfer learningHeather M. Whitney, Karen Drukker, Hiroyuki Abe, Maryellen L. Giger. [doi]
• Individualized and generalized learner models for predicting missed hepatic metastasesParvathy Sudhir Pillai, Scott S. Hsieh, David R. Holmes III, Rickey E. Carter, Joel G. Fletcher, Cynthia H. McCollough. [doi]
• Low-dose CT denoising via CNN trained using images with activation mapMinah Han, Jongduk Baek. [doi]
• Semi-blinded freehand 3D ultrasound with novice usersSatvika Bharadwaj, Komal N. Shah, Yifan Zhao 0001, Aparna Harindranath, Arun George, Kajoli Banerjee Krishnan, Manish Arora. [doi]
• Optimizing model observer performance in learning-based CT reconstructionGregory Ongie, Emil Y. Sidky, Ingrid S. Reiser, Xiaochuan Pan. [doi]
• Task-specific evaluation of clinical pediatric fluoroscopy systemsEmily L. Marshall, Daniela Olivera Velarde, Natalie M. Baughan, Nikolaj Reiser, Chao Guo, Juan-Pablo Cruz-Bastida, Kate A. Feinstein, Ingrid S. Reiser. [doi]
• A task-informed model training method for deep neural network-based image denoisingKaiyan Li 0002, Hua Li 0003, Mark A. Anastasio. [doi]
• Assessment of boundary discrimination performance in a printed phantomCraig K. Abbey, Junyuan Li, Grace J. Gang, J. Webster Stayman. [doi]
• Performance of list mode Hotelling observer and comparison to a neural network observerDan Li, Eric Clarkson. [doi]
• Developing interactive computer-aided detection tools to support translational clinical researchGopichandh Danala, Seyedehnafiseh Mirniaharikandehei, Meredith A. Jones, Tiancheng Gai, Sai Kiran Reddy Maryada, Dee Wu, Yuchen Qiu, Bin Zheng 0001. [doi]
• Evaluating procedures for establishing generative adversarial network-based stochastic image models in medical imagingVarun A. Kelkar, Dimitrios S. Gotsis, Frank J. Brooks, Kyle J. Myers, Prabhat KC, Rongping Zeng, Mark A. Anastasio. [doi]