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@inproceedings{nokey,

title = {Phase Recognition and Cautery Localization in Basal Cell Carcinoma Surgical Videos},

author = {Lucas March and Jessica Rodgers and Amoon Jamzad and Alice Santilli and Rebecca Hisey and Doug McKay and John Rudan and Martin Kaufmann and Kevin Yi Mi Ren and Gabor Fichtinger and Parvin Mousavi},

year  = {2022},

date = {2022-02-20},

urldate = {2022-02-20},

booktitle = {SPIE Medical Imaging, International Society for Optics and Photonics International Society for Optics and Photonics},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@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{santilli2021self,

title = {Self-Supervised Learning For Detection Of Breast Cancer In Surgical Margins With Limited Data},

author = {Alice Santilli and Amoon Jamzad and Alireza Sedghi and Martin Kaufmann and Shaila Merchant and Jay Engel and Kathryn Logan and Julie Wallis and Natasja Janssen and Sonal Varmak and Gabor Fichtinger and John F. Rudan and Parvin Mousavi },

year  = {2021},

date = {2021-04-13},

booktitle = {2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)},

pages = {980--984},

organization = {IEEE},

keywords = {},

pubstate = {published},

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{jamzad2020improved,

title = {Improved Resection Margins in Surgical Oncology Using Intraoperative Mass Spectrometry},

author = {Amoon Jamzad and Alice Santilli and Alireza Sedghi and Natasja N.Y.Janssen and Martin Kaufmann and Kevin Y.M. Ren and Kaitlin Vanderbeck and Ami Wang and Doug McKay and John F. Rudan and Gabor Fichtinger and Parvin Mousavi},

year  = {2020},

date = {2020-09-29},

booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},

journal = {Medical Image Computing and Computer Assisted Interventions},

pages = {44--53},

organization = {MICCAI},

abstract = {PURPOSE: Incomplete tumor resections leads to the presence of cancer cells on the resection margins demanding subsequent revision surgery and poor outcomes for patients. Intraoperative evaluations of the tissue pathology, including the surgical margins, can help decrease the burden of repeat surgeries on the patients and healthcare systems. In this study, we propose adapting multi instance learning (MIL) for prospective and intraoperative basal cell carcinoma (BCC) detection in surgical margins using  mass spectrometry. METHODS: Resected specimens were collected and inspected by a pathologist and burnt with iKnife. Retrospective training data was collected with a standard cautery tip and included 63 BCC and 127 normal burns. Prospective data was collected for testing with both the standard and a fine tip cautery. This included 130 (66 BCC and 64 normal) and 100 (21 BCC and 79 normal) burns, respectively. An attention-based MIL model was adapted and applied to this dataset. RESULTS: Our models were able to predict BCC at surgical margins with AUC as high as 91%. The models were robust to changes in cautery tip but their performance decreased slightly. The models were also tested intraoperatively and achieved an accuracy of 94%. CONCLUSION: This is the first study that applies the concept of MIL for tissue characterization in perioperative and intraoperative REIMS data.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}