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@inproceedings{Jamzad2021c,

title = {Graph Transformers for characterization and interpretation of surgical margins},

author = {Amoon Jamzad and Alice Santilli and Faranak Akbarifar and Martin Kaufmann and Kathryn Logan and Julie Wallis and Kevin Yi Mi Ren and Shaila Merchant and Jay Engel and Sonal Varma and Gabor Fichtinger and John F. Rudan and Parvin Mousavi},

year  = {2021},

date = {2021-09-27},

booktitle = {Medical Image Computing and Computer Assisted Interventions (MICCAI)},

keywords = {},

pubstate = {forthcoming},

tppubtype = {inproceedings}

}

@inproceedings{akbarifar2021graph,

title = {Graph-based analysis of mass spectrometry data for tissue characterization with application in basal cell carcinoma surgery},

author = {Faranak Akbarifar and Amoon Jamzad and Alice Santilli and Martin Kaufmann and Natasja Janssen and Laura Connolly and Kevin Ren and Kaitlin Vanderbeck and Ami Wang and Doug McKay and John F. Rudan and Gabor Fichtinger and Parvin Mousavi},

year  = {2021},

date = {2021-02-15},

booktitle = {Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling},

volume = {11598},

pages = {1159812},

publisher = {SPIE},

organization = {International Society for Optics and Photonics},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{6782196,

title = {Time series analysis of the Twinkling Artifact in color Doppler sonography for surface roughness differentiation: An in vitro feasibility study},

author = {F Akbarifar and A Jamzad and S K Setarehdan},

doi = {10.1109/ICBME.2013.6782196},

year  = {2013},

date = {2013-12-01},

booktitle = {2013 20th Iranian Conference on Biomedical Engineering (ICBME)},

pages = {76-80},

abstract = {Color Doppler Twinkling Artifact (TA) images acquired from the internal body stones contain coded information about the roughness level of the tissue calculi which can be used for treatment management. The TA time series however have never been mathematically studied for roughness identification. This paper investigates the feasibility of estimating the roughness level of a surface by analyzing its TA time series. The TA data of a roughness phantom was used in this study in 2 classes and 1000 TA time series were extracted for each of the classes. Then, three subsets of temporal, spectral, and wavelet features were extracted from each time series. Next, the Bayesian and Support Vector Machines (SVM) classifiers were employed for roughness differentiations. The performance of the proposed method was investigated for cross-comparison of feature subsets, classifiers, and dimension reduction efficiency. Results showed that with only first two principle components projected from the extracted features, an accuracy of 96.06% was obtained which proves the feasibility of roughness recognition by time series analysis of the TA data.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}