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Claudia Mello-Thoms
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2020 – today
- 2024
- [j14]Bulat Ibragimov
, Claudia Mello-Thoms
The Use of Machine Learning in Eye Tracking Studies in Medical Imaging: A Review. IEEE J. Biomed. Health Informatics 28(6): 3597-3612 (2024) - 2023
- [e2]Claudia R. Mello-Thoms, Yan Chen:
Medical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment, San Diego, CA, USA, February 21-23, 2023. SPIE Proceedings 12467, SPIE 2023, ISBN 9781510660397 [contents] - 2022
- [e1]Claudia R. Mello-Thoms, Sian Taylor-Phillips:
Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment, San Diego, CA, USA, February 20-24, 2022 / Online, March 21-27, 2022. SPIE Proceedings 12035, SPIE 2022, ISBN 9781510649453 [contents]
2010 – 2019
- 2019
- [j13]Ziba Gandomkar
Computer-Assisted Nuclear Atypia Scoring of Breast Cancer: a Preliminary Study. J. Digit. Imaging 32(5): 702-712 (2019) - [j12]Suneeta Mall
Can a Machine Learn from Radiologists' Visual Search Behaviour and Their Interpretation of Mammograms - a Deep-Learning Study. J. Digit. Imaging 32(5): 746-760 (2019) - [c22]Suneeta Mall, Elizabeth A. Krupinski, Claudia Mello-Thoms
Missed cancer and visual search of mammograms: what feature based machine-learning can tell us that deep-convolution learning cannot. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment 2019: 1095216 - 2018
- [j11]Ziba Gandomkar
MuDeRN: Multi-category classification of breast histopathological image using deep residual networks. Artif. Intell. Medicine 88: 14-24 (2018) - [c21]Ziba Gandomkar
A framework for distinguishing benign from malignant breast histopathological images using deep residual networks. IWBI 2018: 107180U - [c20]Ziba Gandomkar
A cognitive approach to determine the benefits of pairing radiologists in mammogram reading. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment 2018: 1057704 - [c19]Suneeta Mall, Patrick C. Brennan
A deep (learning) dive into visual search behaviour of breast radiologists. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment 2018: 1057708 - [c18]A. Ganesan, Maram Alakhras, Patrick C. Brennan
Characteristics of the group of radiologists that benefits the most using Breast Screen Reader Assessment Strategy (BREAST). Medical Imaging: Image Perception, Observer Performance, and Technology Assessment 2018: 1057718 - 2017
- [j10]Stephen Littlefair
Does Expectation of Abnormality Affect the Search Pattern of Radiologists When Looking for Pulmonary Nodules? J. Digit. Imaging 30(1): 55-62 (2017) - [j9]Ziba Gandomkar
iCAP: An Individualized Model Combining Gaze Parameters and Image-Based Features to Predict Radiologists' Decisions While Reading Mammograms. IEEE Trans. Medical Imaging 36(5): 1066-1075 (2017) - [c17]Ziba Gandomkar
Determining local and contextual features describing appearance of difficult to identify mitotic figures. Medical Imaging: Digital Pathology 2017: 1014002 - 2016
- [j8]Antje C. Venjakob, Tim Marnitz, Peter Phillips
Image Size Influences Visual Search and Perception of Hemorrhages When Reading Cranial CT: An Eye-Tracking Study. Hum. Factors 58(3): 441-451 (2016) - [c16]Maram Alakhras, Claudia Mello-Thoms
Equivocal Breast Findings Are Reduced with Digital Tomosynthesis. Digital Mammography / IWDM 2016: 89-97 - [c15]Norhashimah Mohd Norsuddin
Lower Recall Rates Reduced Readers' Sensitivity in Screening Mammography. Digital Mammography / IWDM 2016: 116-121 - [c14]John W. Robinson, Patrick C. Brennan
The impact of radiology expertise upon the localization of subtle pulmonary lesions. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment 2016: 97870K - [c13]Christine N. Damases, Claudia Mello-Thoms
Inter-observer variability within BI-RADS and RANZCR mammographic density assessment schemes. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment 2016: 97871C - [c12]Ernest U. Ekpo, Claudia Mello-Thoms
Quantra reproduces BI-RADS assessment on a two-point scale. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment 2016: 978702 - [c11]Mohammad A. Rawashdeh
Investigating the link between the radiological experience and the allocation of an 'equivocal finding'. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment 2016: 978710 - [c10]Ziba Gandomkar
Predicting radiologists' true and false positive decisions in reading mammograms by using gaze parameters and image-based features. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment 2016: 978715 - 2015
- [j7]Sian Taylor-Phillips
Retrospective Review of the Drop in Observer Detection Performance Over Time in Lesion-enriched Experimental Studies. J. Digit. Imaging 28(1): 32-40 (2015) - [j6]Rebecca Randell
Effect of Display Resolution on Time to Diagnosis with Virtual Pathology Slides in a Systematic Search Task. J. Digit. Imaging 28(1): 68-76 (2015) - 2014
- [j5]Frank M. Ligons, Claudia Mello-Thoms
Assessing the impact of cognitive impairment on the usability of an electronic medication delivery unit in an assisted living population. Int. J. Medical Informatics 83(11): 841-848 (2014) - [c9]Dana S. A. L. Mousa, Patrick C. Brennan
Mammographic Density Effect on Readers' Performance and Visual Search Pattern. Digital Mammography / IWDM 2014: 174-180 - [c8]Claudia Mello-Thoms
Understanding the Role of Correct Lesion Assessment in Radiologists' Reporting of Breast Cancer. Digital Mammography / IWDM 2014: 341-347 - [c7]Patrick C. Brennan
BREAST: A Novel Strategy to Improve the Detection of Breast Cancer. Digital Mammography / IWDM 2014: 438-443 - [c6]Mohammad A. Rawashdeh
Mammography: Radiologist and Image Characteristics That Determine the Accuracy of Breast Cancer Diagnosis. Digital Mammography / IWDM 2014: 731-736 - 2011
- [j4]Zach Landis-Lewis, Claudia Mello-Thoms
The feasibility of automating audit and feedback for ART guideline adherence in Malawi. J. Am. Medical Informatics Assoc. 18(6): 868-874 (2011)
2000 – 2009
- 2008
- [c5]Claudia Mello-Thoms
Different Search Patterns and Similar Decision Outcomes: How Can Experts Agree in the Decisions They Make When Reading Digital Mammograms?. Digital Mammography / IWDM 2008: 212-219 - 2007
- [c4]Bin Zheng, Claudia Mello-Thoms
Improvement of Visual Similarity of Similar Breast Masses Selected by Computer-Aided Diagnosis Schemes. ISBI 2007: 516-519 - 2004
- [j3]Brian E. Chapman, Dennis L. Parker, Janet O. Stapelton, Jay S. Tsuruda, Claudia Mello-Thoms
Diagnostic fidelity of the Z-buffer segmentation algorithm: preliminary assessment based on intracranial aneurysm detection. J. Biomed. Informatics 37(1): 19-29 (2004) - [c3]Brian E. Chapman, Claudia Mello-Thoms
Numerical simulations of spectral phase and spectral distance behavior as scalar descriptors of multispectral data. Medical Imaging: Image Processing 2004 - 2003
- [j2]Claudia Mello-Thoms
The Perception of Breast Cancers: What Differentiates Missed from Reported Cancers. IEEE Trans. Medical Imaging 22(10): 1297-1306 (2003) - 2002
- [c2]Brian E. Chapman, Claudia Mello-Thoms:
Image Misregistration and Lesion Signal-difference-to-noise Ratio Impact on Spectral Distance Images Formed from Multispectral Images. AMIA 2002 - [c1]Claudia Mello-Thoms, Calvin F. Nodine, Harold L. Kundel:
What attracts the eye to the location of missed and reported breast cancers? ETRA 2002: 111-117 - 2001
- [j1]Claudia Mello-Thoms
An analysis of perceptual errors in reading mammograms using quasi-local spatial frequency spectra. J. Digit. Imaging 14(3): 117-123 (2001)
Coauthor Index
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