{"id":54,"date":"2019-06-05T20:18:24","date_gmt":"2019-06-05T20:18:24","guid":{"rendered":"http:\/\/flux.cs.queensu.ca\/medi\/?page_id=54"},"modified":"2024-07-27T23:53:16","modified_gmt":"2024-07-27T23:53:16","slug":"publications","status":"publish","type":"page","link":"https:\/\/labs.cs.queensu.ca\/medilab\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"
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337 entries<\/span> «<\/a> ‹<\/a> 1 of 17 ›<\/a> »<\/a> <\/div><\/div>

Farahmand, Mohammad; Jamzad, Amoon; Fooladgar, Fahimeh; Connolly, Laura; Kaufmann, Martin; Ren, Kevin Yi Mi; Rudan, John; McKay, Doug; Fichtinger, Gabor; Mousavi, Parvin<\/p>

FACT: Foundation Model for Assessing Cancer Tissue Margins with Mass Spectrometry<\/a> <\/i> Best Paper<\/span> Journal Article<\/span> <\/p>

In: <\/span>International Journal of Computer Assisted Radiology and Surgery, <\/span>vol. 20, <\/span>no. 6, <\/span>pp. 1097-1104, <\/span>2025<\/span>, ISSN: 1861-6429<\/span>.<\/p>

Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

@article{Farahmand2025,
\r\ntitle = {FACT: Foundation Model for Assessing Cancer Tissue Margins with Mass Spectrometry},
\r\nauthor = {Mohammad Farahmand and Amoon Jamzad and Fahimeh Fooladgar and Laura Connolly and Martin Kaufmann and Kevin Yi Mi Ren and John Rudan and Doug McKay and Gabor Fichtinger and Parvin Mousavi},
\r\nurl = {https:\/\/arxiv.org\/pdf\/2504.11519},
\r\ndoi = {10.1007\/s11548-025-03355-8},
\r\nissn = {1861-6429},
\r\nyear  = {2025},
\r\ndate = {2025-06-01},
\r\nurldate = {2025-06-01},
\r\njournal = {International Journal of Computer Assisted Radiology and Surgery},
\r\nvolume = {20},
\r\nnumber = {6},
\r\npages = {1097-1104},
\r\nabstract = {Accurately classifying tissue margins during cancer surgeries is crucial for ensuring complete tumor removal. Rapid Evaporative Ionization Mass Spectrometry (REIMS), a tool for real-time intraoperative margin assessment, generates spectra that require machine learning models to support clinical decision-making. However, the scarcity of labeled data in surgical contexts presents a significant challenge. This study is the first to develop a foundation model tailored specifically for REIMS data, addressing this limitation and advancing real-time surgical margin assessment.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
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Accurately classifying tissue margins during cancer surgeries is crucial for ensuring complete tumor removal. Rapid Evaporative Ionization Mass Spectrometry (REIMS), a tool for real-time intraoperative margin assessment, generates spectra that require machine learning models to support clinical decision-making. However, the scarcity of labeled data in surgical contexts presents a significant challenge. This study is the first to develop a foundation model tailored specifically for REIMS data, addressing this limitation and advancing real-time surgical margin assessment.<\/div>
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