2024

• Siviengphanom, S., Lewis, S., Brennan, P., Gandomkar, Z. (2024). Computer-extracted global radiomic features can predict the radiologists' first impression about the abnormality of a screening mammogram. British Journal of Radiology, 97(1153), 168-179. [More Information]
• Jiang, Z., Gandomkar, Z., Trieu, P., Tavakoli Taba, S., Barron, M., Obeidy, P., Lewis, S. (2024). Evaluating Recalibrating AI Models for Breast Cancer Diagnosis in a New Context: Insights from Transfer Learning, Image Enhancement and High-Quality Training Data Integration. Cancers, 16(2), 322. [More Information]
• Trieu, P., Barron, M., Jiang, Z., Tavakoli Taba, S., Gandomkar, Z., Lewis, S. (2024). Familiarity, confidence and preference of artificial intelligence feedback and prompts by Australian breast cancer screening readers. Australian Health Review, 48(3), 299-311. [More Information]

2023

• Wong, D., Gandomkar, Z., Lewis, S., Reed, W., Suleiman, M., Siviengphanom, S., Ekpo, E. (2023). Do Reader Characteristics Affect Diagnostic Efficacy in Screening Mammography? A Systematic Review. Clinical Breast Cancer, 23(3), e56-e67. [More Information]
• Gandomkar, Z., Lewis, S., Siviengphanom, S., Wong, D., Ekpo, E., Suleiman, M., Tao, X., Reed, W., Brennan, P. (2023). False-negative diagnosis might occur due to absence of the global radiomic signature of malignancy on screening mammograms. SPIE Medical Imaging 2023, United States: SPIE. [More Information]
• Moradi, S., Omar, A., Zhou, Z., Agostino, A., Gandomkar, Z., Bustamante, H., Power, K., Henderson, R., Leslie, G. (2023). Forecasting and Optimizing Dual Media Filter Performance via Machine Learning. Water Research, 235, 119874. [More Information]
• Siviengphanom, S., Gandomkar, Z., Lewis, S., Brennan, P. (2023). Global Radiomic Features from Mammography for Predicting Difficult-To-Interpret Normal Cases. Journal of Digital Imaging, 36(4), 1541-1552. [More Information]
• Tao, X., Li, T., Gandomkar, Z., Brennan, P., Reed, W. (2023). Incidence, mortality, survival, and disease burden of breast cancer in China compared to other developed countries. Asia-Pacific Journal of Clinical Oncology. [More Information]
• Tao, X., Siviengphanom, S., Gandomkar, Z., Li, T., Reed, W., Brennan, P. (2023). Investigating the error-making patterns in reading high-density screening mammograms between radiologists from two countries. SPIE Medical Imaging 2023, United States: SPIE. [More Information]
• Chen, J., Gandomkar, Z., Reed, W. (2023). Investigating the impact of cognitive biases in radiologists’ image interpretation: A scoping review. European Journal of Radiology, 166, 111013. [More Information]
• Gandomkar, Z., Siviengphanom, S., Suleiman, M., Wong, D., Reed, W., Ekpo, E., Xu, D., Lewis, S., Evans, K., Wolfe, J., Brennan, P. (2023). Reliability of radiologists' first impression when interpreting a screening mammogram. PloS One, 18(2024-04-04 00:00:00). [More Information]
• Campana, A., Gandomkar, Z., Giannotti, N., Reed, W. (2023). The use of radiomics in magnetic resonance imaging for the pre-treatment characterisation of breast cancers: A scoping review. Journal of Medical Radiation Sciences. [More Information]
• Tao, X., Gandomkar, Z., Li, T., Brennan, P., Reed, W. (2023). Using Radiomics-Based Machine Learning to Create Targeted Test Sets to Improve Specific Mammography Reader Cohort Performance: A Feasibility Study. Journal of Personalized Medicine, 13(6), 888. [More Information]

2022

• Gandomkar, Z., Lewis, S., Li, T., Ekpo, E., Brennan, P. (2022). A machine learning model based on readers’ characteristics to predict their performances in reading screening mammograms. Breast Cancer, 29(4), 589-598. [More Information]
• Gandomkar, Z., Lewis, S., Siviengphan, S., Suleiman, M., Wong, D., Reed, W., Ekpo, E., Brennan, P. (2022). Classification and reviewing of prior screening mammograms from screen-detected breast cancer cases. 16th International Workshop on Breast Imaging (IWBI2022), United States: SPIE. [More Information]
• Li, T., Gandomkar, Z., Trieu, P., Lewis, S., Brennan, P. (2022). Differences in lesion interpretation between radiologists in two countries: Lessons from a digital breast tomosynthesis training test set. Asia-Pacific Journal of Clinical Oncology, 18(4), 441-447. [More Information]
• Shishegar, R., Gandomkar, Z., Fallahi, A., Nazem-Zadeh, M., Soltanian-Zadeh, H. (2022). Global and local shape features of the hippocampus based on Laplace–Beltrami eigenvalues and eigenfunctions: a potential application in the lateralization of temporal lobe epilepsy. Neurological Sciences, 43(9), 5543-5552. [More Information]
• Gild, M., Chan, M., Gajera, J., Lurie, B., Gandomkar, Z., Clifton-Bligh, R. (2022). Risk stratification of indeterminate thyroid nodules using ultrasound and machine learning algorithms. Clinical Endocrinology, 96(4), 646-652. [More Information]
• Gandomkar, Z., Siviengphanom, S., Suleiman, M., Wong, D., Reed, W., Ekpo, E., Xu, D., Lewis, S., Brennan, P. (2022). The reliability of radiologists' first impression interpreting a screening mammogram. SPIE Medical Imaging 2022, United States: SPIE. [More Information]
• Li, T., Li, J., Heard, R., Gandomkar, Z., Ren, J., Dai, M., Brennan, P. (2022). Understanding mammographic breast density profile in China: A Sino-Australian comparative study of breast density using real-world data from cancer screening programs. Asia-Pacific Journal of Clinical Oncology, 18(6), 696-705. [More Information]
• Gandomkar, Z., Khong, P., Punch, A., Lewis, S. (2022). Using Occlusion-Based Saliency Maps to Explain an Artificial Intelligence Tool in Lung Cancer Screening: Agreement Between Radiologists, Labels, and Visual Prompts. Journal of Digital Imaging, 35(5), 1164-1175. [More Information]
• Tao, X., Gandomkar, Z., Li, T., Reed, W., Brennan, P. (2022). Varying performance levels for diagnosing mammographic images depending on reader nationality have AI and educational implications. Progress in Biomedical Optics and Imaging - Proceedings of SPIE, : SPIE. [More Information]

2021

• DeMott, R., Haghdadi, N., Kong, C., Gandomkar, Z., Kenney, M., Collins, P., Primig, S. (2021). 3D electron backscatter diffraction characterization of fine α titanium microstructures: collection, reconstruction, and analysis methods. Ultramicroscopy, 230, 113394. [More Information]
• Gandomkar, Z., Li, T., Trieu, P., Lewis, S., Brennan, P. (2021). A retrospective comparative study of reading performances between radiologists from two countries in the assessment of 3D mammography. Medical Imaging 2021: Image Perception, Observer Performance, and Technology Assessment, United States: SPIE. [More Information]
• Gandomkar, Z., Ekpo, E., Lewis, S., Suleiman, M., Siviengphanom, S., Li, T., Brennan, P. (2021). An end-to-end deep learning model can detect the gist of the abnormal in prior mammograms as perceived by experienced radiologists. Medical Imaging 2021: Image Perception, Observer Performance, and Technology Assessment, United States: SPIE. [More Information]
• Kanbayti, I., Rae, W., McEntee, M., Gandomkar, Z., Ekpo, E. (2021). Clinicopathologic breast cancer characteristics: predictions using global textural features of the ipsilateral breast mammogram. Radiological Physics and Technology, 14(3), 248-261. [More Information]
• DeMott, R., Haghdadi, N., Gandomkar, Z., Liao, X., Ringer, S., Primig, S. (2021). Formation and 3D morphology of interconnected α microstructures in additively manufactured Ti-6Al-4V. Materialia, 20, 101201. [More Information]
• Gandomkar, Z., Siviengphanom, S., Ekpo, E., Suleiman, M., Tavakoli Taba, S., Li, T., Xu, D., Evans, K., Lewis, S., Wolfe, J., Brennan, P. (2021). Global processing provides malignancy evidence complementary to the information captured by humans or machines following detailed mammogram inspection. Scientific Reports, 11(1), 20122. [More Information]
• Siviengphanom, S., Gandomkar, Z., Lewis, S., Brennan, P. (2021). Mammography-based radiomics in breast cancer: a scoping review of current knowledge and future needs. Academic Radiology, In press, 1-20. [More Information]

2020

• Wong, D., Gandomkar, Z., Wu, W., Guijing, Z., Gao, W., He, X., Wang, Y., Reed, W. (2020). Artificial intelligence and convolution neural networks assessing mammographic images: a narrative literature review. Journal of Medical Radiation Sciences, 67(2), 134-142. [More Information]
• Gandomkar, Z., Li, T., Shao, Z., Tang, L., Xiao, Q., Gu, Y., Di, G., Lewis, S., Mello-Thoms, C., Brennan, P. (2020). Breast cancer risk prediction in Chinese women based on mammographic texture and a comprehensive set of epidemiologic factors. 15th International Workshop on Breast Imaging (IWBI 2020), Bellingham: Society of Photo-Optical Instrumentation Engineers (SPIE). [More Information]
• Lewis, S., Huang, K., Nguyen, T., Gandomkar, Z., Norsuddin, N., Mello-Thoms, C. (2020). Characteristics of frequently recalled false positive cases in screening mammography. 15th International Workshop on Breast Imaging (IWBI 2020), Bellingham: Society of Photo-Optical Instrumentation Engineers (SPIE). [More Information]
• Gandomkar, Z., Ekpo, E., Chen, Z., Suleiman, M., Li, T., Tapia, K., Brennan, P., Lewis, S. (2020). Expert radiologist performance does not appear to impact upon their capability in perceiving the gist of the abnormal on mammograms. 15th International Workshop on Breast Imaging (IWBI 2020), Bellingham: Society of Photo-Optical Instrumentation Engineers (SPIE). [More Information]
• Gandomkar, Z., Ekpo, E., Lewis, S., Suleiman, M., Tapia, K., Li, T., Tavakoli Taba, S., Trieu, P., Brennan, P. (2020). Investigating the potential of a gist-sensitive computer-aided detection tool. Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment, Bellingham: Society of Photo-Optical Instrumentation Engineers (SPIE). [More Information]
• Moradi, S., Agostino, A., Gandomkar, Z., Kim, S., Hamilton, L., Sharma, A., Henderson, R., Leslie, G. (2020). Quantifying natural organic matter concentration in water from climatological parameters using different machine learning algorithms. H2Open Journal, 3(1), 328-342. [More Information]

2019

• Gandomkar, Z., Suleiman, M., Demchig, D., Brennan, P., McEntee, M. (2019). BI-RADS density categorization using deep neural networks. Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment, Bellingham: Society of Photo-Optical Instrumentation Engineers (SPIE). [More Information]
• Li, T., Tang, L., Gandomkar, Z., Heard, R., Mello-Thoms, C., Xiao, Q., Gu, Y., Di, G., Nickson, C., Shao, Z., Brennan, P. (2019). Characteristics of Mammographic Breast Density and Associated Factors for Chinese Women: Results from an Automated Measurement. Journal of Oncology, 2019, 1-9. [More Information]
• Gandomkar, Z., Brennan, P., Mello-Thoms, C. (2019). Computer-Assisted Nuclear Atypia Scoring of Breast Cancer: a Preliminary Study. Journal of Digital Imaging, 32(5), 702-712. [More Information]
• Gandomkar, Z., Ekpo, E., Lewis, S., Evans, K., Tapia, K., Trieu, P., Wolfe, J., Brennan, P. (2019). Does the strength of the GIST signal predict the difficulty of breast cancer detection in usual presentation and reporting mechanisms? Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment, Bellingham: Society of Photo-Optical Instrumentation Engineers (SPIE). [More Information]
• Malek, M., Rahmani, M., Seyyed Ebrahimi, S., Tabibian, E., Alidoosti, A., Rahimifar, P., Akhavan, S., Gandomkar, Z. (2019). Investigating the diagnostic value of quantitative parameters based on T2-weighted and contrast-enhanced MRI with psoas muscle and outer myometrium as internal references for differentiating uterine sarcomas from leiomyomas at 3T MRI. Cancer Imaging, 19(1), 1-10. [More Information]
• Gandomkar, Z., Mello-Thoms, C. (2019). Visual search in breast imaging. British Journal of Radiology, 92(1102), 1-15. [More Information]

2018

• Gandomkar, Z., Brennan, P., Mello-Thoms, C. (2018). A cognitive approach to determine the benefits of pairing radiologists in mammogram reading. SPIE Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment, Bellingham: Society of Photo-Optical Instrumentation Engineers (SPIE). [More Information]
• Gandomkar, Z., Brennan, P., Mello-Thoms, C. (2018). A framework for distinguishing benign from malignant breast histopathological images using deep residual networks. 14th International Workshop on Breast Imaging (IWBI 2018), Bellingham: Society of Photo-Optical Instrumentation Engineers (SPIE). [More Information]
• Gandomkar, Z., Tay, K., Brennan, P., Kozuch, E., Mello-Thoms, C. (2018). Can eye-tracking metrics be used to better pair radiologists in a mammogram reading task? Medical Physics, 45(11), 4844-4856. [More Information]
• Gandomkar, Z., Ekpo, E., Lewis, S., Evans, K., Tapia, K., Trieu, P., Wolfe, J., Brennan, P. (2018). Detection of the abnormal GIST in the prior mammograms even with no overt sign of breast cancer. 14th International Workshop on Breast Imaging (IWBI 2018), Bellingham: Society of Photo-Optical Instrumentation Engineers (SPIE). [More Information]
• Li, T., Tang, L., Gandomkar, Z., Heard, R., Mello-Thoms, C., Shao, Z., Brennan, P. (2018). Mammographic density and other risk factors for breast cancer among women in China. The Breast Journal, 24(3), 426-428. [More Information]
• Gandomkar, Z., Brennan, P., Mello-Thoms, C. (2018). MuDeRN: Multi-category classification of breast histopathological image using deep residual networks. Artificial Intelligence in Medicine, 88, 14-24. [More Information]
• Brennan, P., Gandomkar, Z., Ekpo, E., Tapia, K., Trieu, P., Lewis, S., Wolfe, J., Evans, K. (2018). RadioloGISTs can detect the 'GIST' of breast cancer before any overt signs of cancer appear. Scientific Reports, 8(1), 1-12. [More Information]
• Gandomkar, Z., Tay, K., Brennan, P., Mello-Thoms, C. (2018). Recurrence quantification analysis of radiologists' scanpaths when interpreting mammograms. Medical Physics, 45(7), 3052-3062. [More Information]
• Gandomkar, M., Sarang, R., Gandomkar, Z. (2018). TrainingPal: An Algorithm for Recognition and Counting Popular Exercises Using Smartphone Sensors. 26th Iranian Conference on Electrical Engineering (ICEE 2018), Mashhad: Institute of Electrical and Electronics Engineers (IEEE). [More Information]

2017

• Gandomkar, Z., Tay, K., Brennan, P., Mello-Thoms, C. (2017). A model based on temporal dynamics of fixations for distinguishing expert radiologists' scan paths. SPIE Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment. [More Information]
• Demchig, D., Gandomkar, Z., Brennan, P. (2017). Automatic segmentation of the dense tissue in digital mammograms for BIRADS density categorization. Medical Image Perception Society (MIPS) XVII Conference 2017, United States: S P I E - International Society for Optical Engineering.
• Gandomkar, Z., Brennan, P., Mello-Thoms, C. (2017). Determining image processing features describing the appearance of challenging mitotic figures and miscounted nonmitotic objects. Journal of Pathology Informatics, 8(34), 1-10. [More Information]
• Gandomkar, Z., Brennan, P., Mello-Thoms, C. (2017). Determining local and contextual features describing appearance of less easily identifiable mitotic figures. SPIE Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment. [More Information]
• Gandomkar, Z., Tay, K., Ryder, W., Brennan, P., Mello-Thoms, C. (2017). iCAP: an individualized model combining gaze parameters and image-based features to predict radiologists' decisions while reading mammograms. IEEE Transactions on Medical Imaging, 36(5), 1066-1075. [More Information]

2016

• Gandomkar, Z., Brennan, P., Mello-Thoms, C. (2016). Computer-based image analysis in breast pathology. Journal of Pathology Informatics, 7(1), 1-12. [More Information]
• Gandomkar, Z., Tay, K., Ryder, W., Brennan, P., Mello-Thoms, C. (2016). Predicting radiologists' true and false positive decisions in reading mammograms by using gaze parameters and image-based features. Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment, Bellingham: Society of Photo-Optical Instrumentation Engineers (SPIE). [More Information]

2015

• Gandomkar, Z., Tay, K., Ryder, W., Brennan, P., Mello-Thoms, C. (2015). iDensity: an automatic Gabor filter-based algorithm for breast density assessment. SPIE Medical Imaging 2015: Image Perception, Observer Performance, and Technology Assessment, Washington USA: SPIE Publications. [More Information]

2014

• Sadouni, M., Gandomkar, Z., Arbabi, E. (2014). EEG-based sympathy recognition. International Journal of Medical Engineering and Informatics, 6(1), 14-25. [More Information]
• Gandomkar, Z., Bahrami, F. (2014). Method to classify elderly subjects as fallers and non-fallers based on gait energy image. Healthcare Technology Letters, 1(3), 110-114. [More Information]