Ziba Gandomkar

Orcid: 0000-0001-6480-3572

According to our database1, Ziba Gandomkar authored at least 25 papers between 2012 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2023
Global Radiomic Features from Mammography for Predicting Difficult-To-Interpret Normal Cases.
J. Digit. Imaging, August, 2023

Investigating the error-making patterns in reading high-density screening mammograms between radiologists from two countries.
Proceedings of the Medical Imaging 2023: Image Perception, 2023

Global mammographic radiomic signature can predict radiologists' difficult-to-interpret normal cases.
Proceedings of the Medical Imaging 2023: Image Perception, 2023

How do you solve a problem like concordance? a study of radiologists' clinical annotations for mammographic AI training.
Proceedings of the Medical Imaging 2023: Image Perception, 2023

False-negative diagnosis might occur due to absence of the global radiomic signature of malignancy on screening mammograms.
Proceedings of the Medical Imaging 2023: Image Perception, 2023

A comparative study of diagnostic performance and work experience of radiologists in three countries interpreting digital breast tomosynthesis.
Proceedings of the Medical Imaging 2023: Image Perception, 2023

2022
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, 2022

Varying performance levels for diagnosing mammographic images depending on reader nationality have AI and educational implications.
Proceedings of the Medical Imaging 2022: Image Perception, 2022

The reliability of radiologists' first impression interpreting a screening mammogram.
Proceedings of the Medical Imaging 2022: Image Perception, 2022

2021
A retrospective comparative study of reading performances between radiologists from two countries in the assessment of 3D mammography.
Proceedings of the Medical Imaging 2021: Image Perception, 2021

An end-to-end deep learning model can detect the gist of the abnormal in prior mammograms as perceived by experienced radiologists.
Proceedings of the Medical Imaging 2021: Image Perception, 2021

2020
Investigating the potential of a gist-sensitive computer-aided detection tool.
Proceedings of the Medical Imaging 2020: Image Perception, 2020

2019
Computer-Assisted Nuclear Atypia Scoring of Breast Cancer: a Preliminary Study.
J. Digit. Imaging, 2019

BI-RADS density categorization using deep neural networks.
Proceedings of the Medical Imaging 2019: Image Perception, 2019

Does the strength of the gist signal predict the difficulty of breast cancer detection in usual presentation and reporting mechanisms?
Proceedings of the Medical Imaging 2019: Image Perception, 2019

2018
Added benefits of computer-assisted analysis of Hematoxylin-Eosin stained breast histopathological digital slides.
PhD thesis, 2018

MuDeRN: Multi-category classification of breast histopathological image using deep residual networks.
Artif. Intell. Medicine, 2018

A cognitive approach to determine the benefits of pairing radiologists in mammogram reading.
Proceedings of the Medical Imaging 2018: Image Perception, 2018

Detection of the abnormal gist in the prior mammograms even with no overt sign of breast cancer.
Proceedings of the 14th International Workshop on Breast Imaging, 2018

A framework for distinguishing benign from malignant breast histopathological images using deep residual networks.
Proceedings of the 14th International Workshop on Breast Imaging, 2018

2017
iCAP: An Individualized Model Combining Gaze Parameters and Image-Based Features to Predict Radiologists' Decisions While Reading Mammograms.
IEEE Trans. Medical Imaging, 2017

Determining local and contextual features describing appearance of difficult to identify mitotic figures.
Proceedings of the Medical Imaging 2017: Digital Pathology, 2017

2016
Predicting radiologists' true and false positive decisions in reading mammograms by using gaze parameters and image-based features.
Proceedings of the Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment, San Diego, California, United States, 27 February, 2016

2014
EEG-based sympathy recognition.
Int. J. Medical Eng. Informatics, 2014

2012
Hippocampal shape analysis in the Laplace Beltrami feature space for temporal lobe epilepsy diagnosis and lateralization.
Proceedings of the 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2012


  Loading...