{"id":2255,"date":"2024-08-12T14:29:43","date_gmt":"2024-08-12T14:29:43","guid":{"rendered":"https:\/\/labs.cs.queensu.ca\/medilab\/?post_type=qsc_member&p=2255"},"modified":"2026-09-14T05:29:50","modified_gmt":"2026-09-14T05:29:50","slug":"mohammad-farahmand","status":"publish","type":"qsc_member","link":"https:\/\/labs.cs.queensu.ca\/medilab\/members\/mohammad-farahmand\/","title":{"rendered":"Mohammad “Aryan” Farahmand"},"content":{"rendered":"
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Mohammad “Aryan” Farahmand<\/h1><\/div>\n\t\t
PhD Student<\/div>\n\t\t
School of Computing<\/div>\n\t\t
Queen’s University<\/div>\n\t\t
Member from 2023<\/em> to present<\/em><\/div>\n\t\t
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Publications<\/h2>\n\n\n\n

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Liu, Sophia; Jamzad, Amoon; Farahmand, Mohammad; Mousavi, Parvin<\/p>

Integrating Foundation Models into MassVision for Mass Spectrometry Imaging Journal Article<\/span> <\/p>

In: <\/span>Inquiry@ Queen's Undergraduate Research Conference Proceedings, <\/span>vol. 20, <\/span>no. 2, <\/span>2026<\/span>.<\/p>

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

@article{liu2026,
\r\ntitle = {Integrating Foundation Models into MassVision for Mass Spectrometry Imaging},
\r\nauthor = {Sophia Liu and Amoon Jamzad and Mohammad Farahmand and Parvin Mousavi},
\r\nyear  = {2026},
\r\ndate = {2026-01-01},
\r\njournal = {Inquiry@ Queen's Undergraduate Research Conference Proceedings},
\r\nvolume = {20},
\r\nnumber = {2},
\r\nabstract = {Mass spectrometry imaging (MSI) combines mass spectrometry with spatial information to map the distribution of molecules across tissue samples. This provides insight into biochemical changes associated with diseases such as cancer. MassVision, a 3D Slicer module developed in the Med-i Lab, offers tools for the visualization and analysis of MSI datasets. The high dimensionality and complexity of MSI data, however, can make interpretation challenging. Foundation models offer a potential way to address this challenge by learning from large-scale datasets and converting complex data into simplified representations to identify meaningful patterns. This project investigates the introduction of foundation models into the MassVision workflow.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
Mass spectrometry imaging (MSI) combines mass spectrometry with spatial information to map the distribution of molecules across tissue samples. This provides insight into biochemical changes associated with diseases such as cancer. MassVision, a 3D Slicer module developed in the Med-i Lab, offers tools for the visualization and analysis of MSI datasets. The high dimensionality and complexity of MSI data, however, can make interpretation challenging. Foundation models offer a potential way to address this challenge by learning from large-scale datasets and converting complex data into simplified representations to identify meaningful patterns. This project investigates the introduction of foundation models into the MassVision workflow.<\/div>
Close<\/a><\/p><\/div><\/div><\/div>
 Maghsoodi, Nooshin;  Jamzad, Amoon;  Policelli, Robert;  Farahmand, Mohammad;  Srikanthan, Dilakshan;  Kaufmann, Martin;  Ren, Kevin YM;  Merchant, Shaila;  Varma, Sonal;  Walker, Ross;  McKay, Doug;  Rudan, John;  Fichtinger, Gabor;  Mousavi, Parvin<\/p>
Agent-Guided Relational Concept Discovery: Toward Interpretable Surgical Margin Assessment Journal Article<\/span> <\/p>
In: <\/span>arXiv preprint arXiv:2607.21437, <\/span>2026<\/span>.<\/p>
Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>
@article{maghsoodi2026,
\r\ntitle = {Agent-Guided Relational Concept Discovery: Toward Interpretable Surgical Margin Assessment},
\r\nauthor = {Nooshin Maghsoodi and Amoon Jamzad and Robert Policelli and Mohammad Farahmand and Dilakshan Srikanthan and Martin Kaufmann and Kevin YM Ren and Shaila Merchant and Sonal Varma and Ross Walker and Doug McKay and John Rudan and Gabor Fichtinger and Parvin Mousavi},
\r\nyear  = {2026},
\r\ndate = {2026-01-01},
\r\njournal = {arXiv preprint arXiv:2607.21437},
\r\nabstract = {Deep learning models can effectively use Rapid Evaporative Ionization Mass Spectrometry (REIMS) data for surgical margin assessment. However, their clinical adoption remains challenging due to limited generalization to operating room conditions. This difficulty arises because models are typically trained on labeled spectra collected from resected tissue samples, while they must operate on noisy, unlabeled data acquired directly during surgery. In addition, the black-box nature of deep learning models makes it difficult to understand and systematically improve their behavior. Concept-based learning offers a promising way to address these challenges by mapping raw measurements to human-understandable concepts. However, supervised concept-based approaches rely on concept annotations, which are difficult to obtain in complex mass spectrometry workflows. We propose Agent-Guided Concept Discovery, a framework that learns meaningful concepts directly from data without requiring predefined concept labels. During training, a reasoning agent refines semantic descriptions of the learned concepts and adaptively adjusts their weight based on diagnostic relevance. These concepts are further grounded using a biochemical knowledge graph to ensure consistency with known metabolic relationships. Across Skin and Breast Cancer datasets, our model improves balanced accuracy and sensitivity over the baseline. In a representative intraoperative case, it shows fewer false positives, indicating better generalization to surgical conditions.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
Deep learning models can effectively use Rapid Evaporative Ionization Mass Spectrometry (REIMS) data for surgical margin assessment. However, their clinical adoption remains challenging due to limited generalization to operating room conditions. This difficulty arises because models are typically trained on labeled spectra collected from resected tissue samples, while they must operate on noisy, unlabeled data acquired directly during surgery. In addition, the black-box nature of deep learning models makes it difficult to understand and systematically improve their behavior. Concept-based learning offers a promising way to address these challenges by mapping raw measurements to human-understandable concepts. However, supervised concept-based approaches rely on concept annotations, which are difficult to obtain in complex mass spectrometry workflows. We propose Agent-Guided Concept Discovery, a framework that learns meaningful concepts directly from data without requiring predefined concept labels. During training, a reasoning agent refines semantic descriptions of the learned concepts and adaptively adjusts their weight based on diagnostic relevance. These concepts are further grounded using a biochemical knowledge graph to ensure consistency with known metabolic relationships. Across Skin and Breast Cancer datasets, our model improves balanced accuracy and sensitivity over the baseline. In a representative intraoperative case, it shows fewer false positives, indicating better generalization to surgical conditions.<\/div>
Close<\/a><\/p><\/div><\/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}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
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>
Close<\/a><\/p><\/div>