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Ziba Gandomkar
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2020 – today
- 2024
- [j7]Marnee J. McKay
, Kenneth A. Weber II
MuscleMap: An Open-Source, Community-Supported Consortium for Whole-Body Quantitative MRI of Muscle. J. Imaging 10(11): 262 (2024) - 2023
- [j6]Somphone Siviengphanom
Global Radiomic Features from Mammography for Predicting Difficult-To-Interpret Normal Cases. J. Digit. Imaging 36(4): 1541-1552 (2023) - [c18]Elly K. Gallagher, Phuong Dung Trieu, Ziba Gandomkar, Tong Li, Masoumeh Gity, Patrick C. Brennan, Sarah Lewis, Seyedamir Tavakoli Taba:
A comparative study of diagnostic performance and work experience of radiologists in three countries interpreting digital breast tomosynthesis. Image Perception, Observer Performance, and Technology Assessment 2023 - [c17]Ziba Gandomkar, Sarah J. Lewis, Somphone Siviengphanom, Dennis Wong, Ernest U. Ekpo, Moayyad E. Suleiman
False-negative diagnosis might occur due to absence of the global radiomic signature of malignancy on screening mammograms. Image Perception, Observer Performance, and Technology Assessment 2023 - [c16]Zhengqiang Jiang, Phuong Dung Trieu, Ziba Gandomkar, Seyedamir Tavakoli Taba, Melissa L. Barron, Sarah J. Lewis:
How do you solve a problem like concordance? a study of radiologists' clinical annotations for mammographic AI training. Image Perception, Observer Performance, and Technology Assessment 2023 - [c15]Somphone Siviengphanom, Ziba Gandomkar, Sarah J. Lewis, Patrick C. Brennan:
Global mammographic radiomic signature can predict radiologists' difficult-to-interpret normal cases. Image Perception, Observer Performance, and Technology Assessment 2023 - [c14]Xuetong Tao, Somphone Siviengphanom, Ziba Gandomkar, Tong Li, Warren M. Reed
Investigating the error-making patterns in reading high-density screening mammograms between radiologists from two countries. Image Perception, Observer Performance, and Technology Assessment 2023 - 2022
- [j5]Ziba Gandomkar, Pek-Lan Khong, Amanda Punch, Sarah Lewis
Using Occlusion-Based Saliency Maps to Explain an Artificial Intelligence Tool in Lung Cancer Screening: Agreement Between Radiologists, Labels, and Visual Prompts. J. Digit. Imaging 35(5): 1164-1175 (2022) - [c13]Ziba Gandomkar, Somphone Siviengphanom, Moayyad E. Suleiman
The reliability of radiologists' first impression interpreting a screening mammogram. Image Perception, Observer Performance, and Technology Assessment 2022 - [c12]Xuetong Tao, Ziba Gandomkar, Tong Li
Varying performance levels for diagnosing mammographic images depending on reader nationality have AI and educational implications. Image Perception, Observer Performance, and Technology Assessment 2022 - 2021
- [c11]Ziba Gandomkar
An end-to-end deep learning model can detect the gist of the abnormal in prior mammograms as perceived by experienced radiologists. Image Perception, Observer Performance, and Technology Assessment 2021 - [c10]Ziba Gandomkar
A retrospective comparative study of reading performances between radiologists from two countries in the assessment of 3D mammography. Image Perception, Observer Performance, and Technology Assessment 2021 - 2020
- [c9]Ziba Gandomkar
Investigating the potential of a gist-sensitive computer-aided detection tool. Image Perception, Observer Performance, and Technology Assessment 2020: 113160J
2010 – 2019
- 2019
- [j4]Ziba Gandomkar
Computer-Assisted Nuclear Atypia Scoring of Breast Cancer: a Preliminary Study. J. Digit. Imaging 32(5): 702-712 (2019) - [c8]Ziba Gandomkar
BI-RADS density categorization using deep neural networks. Image Perception, Observer Performance, and Technology Assessment 2019: 109520N - [c7]Ziba Gandomkar
Does the strength of the gist signal predict the difficulty of breast cancer detection in usual presentation and reporting mechanisms? Image Perception, Observer Performance, and Technology Assessment 2019: 1095203 - 2018
- [b1]Ziba Gandomkar:
Added benefits of computer-assisted analysis of Hematoxylin-Eosin stained breast histopathological digital slides. University of Sydney, Australia, 2018 - [j3]Ziba Gandomkar
MuDeRN: Multi-category classification of breast histopathological image using deep residual networks. Artif. Intell. Medicine 88: 14-24 (2018) - [c6]Ziba Gandomkar
A framework for distinguishing benign from malignant breast histopathological images using deep residual networks. IWBI 2018: 107180U - [c5]Ziba Gandomkar
Detection of the abnormal gist in the prior mammograms even with no overt sign of breast cancer. IWBI 2018: 1071804 - [c4]Ziba Gandomkar
A cognitive approach to determine the benefits of pairing radiologists in mammogram reading. Image Perception, Observer Performance, and Technology Assessment 2018: 1057704 - 2017
- [j2]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) - [c3]Ziba Gandomkar
Determining local and contextual features describing appearance of difficult to identify mitotic figures. Digital Pathology 2017: 1014002 - 2016
- [c2]Ziba Gandomkar
Predicting radiologists' true and false positive decisions in reading mammograms by using gaze parameters and image-based features. Image Perception, Observer Performance, and Technology Assessment 2016: 978715 - 2014
- [j1]Muhammad Ali Sadouni, Ziba Gandomkar
EEG-based sympathy recognition. Int. J. Medical Eng. Informatics 6(1): 14-25 (2014) - 2012
- [c1]Rosita Shishegar, Ziba Gandomkar
Hippocampal shape analysis in the Laplace Beltrami feature space for temporal lobe epilepsy diagnosis and lateralization. ISBI 2012: 150-153
Coauthor Index
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last updated on 2024-12-02 21:30 CET by the dblp team
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