Hippocampal shape analysis in the Laplace Beltrami feature space for temporal lobe epilepsy diagnosis and lateralization. - Rosita Shishegar; Ziba Gandomkar; Hamid Soltaman-Zadeh; Seyed Reza Moghadasi (ISBI, 2012)

EEG-based sympathy recognition. - Muhammad Ali Sadouni; Ziba Gandomkar; Ehsan Arbabi (Int. J. Medical Eng. Informatics, 2014)

Predicting radiologists' true and false positive decisions in reading mammograms by using gaze parameters and image-based features. - Ziba Gandomkar; Kevin Tay; Will Ryder; Patrick C. Brennan; Claudia Mello-Thoms (Image Perception, Observer Performance, and Technology Assessment, 2016)

Determining local and contextual features describing appearance of difficult to identify mitotic figures. - Ziba Gandomkar; Patrick C. Brennan; Claudia Mello-Thoms (Digital Pathology, 2017)

iCAP: An Individualized Model Combining Gaze Parameters and Image-Based Features to Predict Radiologists' Decisions While Reading Mammograms. - Ziba Gandomkar; Kevin Tay; Will Ryder; Patrick C. Brennan; Claudia Mello-Thoms (IEEE Trans. Medical Imaging, 2017)

A framework for distinguishing benign from malignant breast histopathological images using deep residual networks. - Ziba Gandomkar; Patrick C. Brennan; Claudia Mello-Thoms (IWBI, 2018)

Detection of the abnormal gist in the prior mammograms even with no overt sign of breast cancer. - Ziba Gandomkar; Ernest U. Ekpo; Sarah J. Lewis 001; Karla K. Evans; Kriscia Tapia; Phuong Dung Trieu; Jeremy M. Wolfe; Patrick C. Brennan (IWBI, 2018)

A cognitive approach to determine the benefits of pairing radiologists in mammogram reading. - Ziba Gandomkar; Patrick C. Brennan; Claudia Mello-Thoms (Image Perception, Observer Performance, and Technology Assessment, 2018)

MuDeRN: Multi-category classification of breast histopathological image using deep residual networks. - Ziba Gandomkar; Patrick C. Brennan; Claudia Mello-Thoms (Artif. Intell. Medicine, 2018)

Added benefits of computer-assisted analysis of Hematoxylin-Eosin stained breast histopathological digital slides. - Ziba Gandomkar (2018)

Does the strength of the gist signal predict the difficulty of breast cancer detection in usual presentation and reporting mechanisms? - Ziba Gandomkar; Ernest U. Ekpo; Sarah J. Lewis 001; Karla K. Evans; Kriscia A. Tapia; Phuong Dung Trieu; Jeremy M. Wolfe; Patrick C. Brennan (Image Perception, Observer Performance, and Technology Assessment, 2019)

BI-RADS density categorization using deep neural networks. - Ziba Gandomkar; Moayyad E. Suleiman; Delgermaa Demchig; Patrick C. Brennan; Mark F. McEntee (Image Perception, Observer Performance, and Technology Assessment, 2019)

Computer-Assisted Nuclear Atypia Scoring of Breast Cancer: a Preliminary Study. - Ziba Gandomkar; Patrick C. Brennan; Claudia Mello-Thoms (J. Digit. Imaging, 2019)

Investigating the potential of a gist-sensitive computer-aided detection tool. - Ziba Gandomkar; Ernest U. Ekpo; Sarah J. Lewis 001; Moayyad E. Suleiman; Kriscia Tapia; Tong Li; Seyedamir Tavakoli Taba; Phuong Dung Trieu; Patrick C. Brennan (Image Perception, Observer Performance, and Technology Assessment, 2020)

An end-to-end deep learning model can detect the gist of the abnormal in prior mammograms as perceived by experienced radiologists. - Ziba Gandomkar; Ernest U. Ekpo; Sarah J. Lewis 001; Moayyad E. Suleiman; Somphone Siviengphanom; Tong Li; Patrick C. Brennan (Image Perception, Observer Performance, and Technology Assessment, 2021)

A retrospective comparative study of reading performances between radiologists from two countries in the assessment of 3D mammography. - Ziba Gandomkar; Tong Li; Phuong Dung Trieu; Sarah J. Lewis 001; Patrick C. Brennan (Image Perception, Observer Performance, and Technology Assessment, 2021)

The reliability of radiologists' first impression interpreting a screening mammogram. - Ziba Gandomkar; Somphone Siviengphanom; Moayyad E. Suleiman; Dennis Wong; Warren M. Reed; Ernest U. Ekpo; Dong Xu; Sarah J. Lewis 001; Patrick C. Brennan (Image Perception, Observer Performance, and Technology Assessment, 2022)

Varying performance levels for diagnosing mammographic images depending on reader nationality have AI and educational implications. - Xuetong Tao; Ziba Gandomkar; Tong Li; Warren M. Reed; Patrick C. Brennan (Image Perception, Observer Performance, and Technology Assessment, 2022)

Using Occlusion-Based Saliency Maps to Explain an Artificial Intelligence Tool in Lung Cancer Screening: Agreement Between Radiologists, Labels, and Visual Prompts. - Ziba Gandomkar; Pek-Lan Khong; Amanda Punch; Sarah Lewis 001 (J. Digit. Imaging, 2022)

A comparative study of diagnostic performance and work experience of radiologists in three countries interpreting digital breast tomosynthesis. - Elly K. Gallagher; Phuong Dung Trieu; Ziba Gandomkar; Tong Li; Masoumeh Gity; Patrick C. Brennan; Sarah Lewis 001; Seyedamir Tavakoli Taba (Image Perception, Observer Performance, and Technology Assessment, 2023)

False-negative diagnosis might occur due to absence of the global radiomic signature of malignancy on screening mammograms. - Ziba Gandomkar; Sarah J. Lewis 001; Somphone Siviengphanom; Dennis Wong; Ernest U. Ekpo; Moayyad E. Suleiman; Xuetong Tao; Warren M. Reed; Patrick C. Brennan (Image Perception, Observer Performance, and Technology Assessment, 2023)

How do you solve a problem like concordance? a study of radiologists' clinical annotations for mammographic AI training. - Zhengqiang Jiang; Phuong Dung Trieu; Ziba Gandomkar; Seyedamir Tavakoli Taba; Melissa L. Barron; Sarah J. Lewis 001 (Image Perception, Observer Performance, and Technology Assessment, 2023)

Global mammographic radiomic signature can predict radiologists' difficult-to-interpret normal cases. - Somphone Siviengphanom; Ziba Gandomkar; Sarah J. Lewis 001; Patrick C. Brennan (Image Perception, Observer Performance, and Technology Assessment, 2023)

Investigating the error-making patterns in reading high-density screening mammograms between radiologists from two countries. - Xuetong Tao; Somphone Siviengphanom; Ziba Gandomkar; Tong Li; Warren M. Reed; Patrick C. Brennan (Image Perception, Observer Performance, and Technology Assessment, 2023)

Global Radiomic Features from Mammography for Predicting Difficult-To-Interpret Normal Cases. - Somphone Siviengphanom; Ziba Gandomkar; Sarah J. Lewis 001; Patrick C. Brennan (J. Digit. Imaging, 2023)

MuscleMap: An Open-Source, Community-Supported Consortium for Whole-Body Quantitative MRI of Muscle. - Marnee J. McKay; Kenneth Arnold Weber II; Evert O. Wesselink; Zachary A. Smith; Rebecca Abbott; David B. Anderson; Claire E. Ashton-James; John Atyeo; Aaron J. Beach; Joshua Burns; Stephen Clarke; Natalie J. Collins; et al. (J. Imaging, 2024)