Publications

@article{wilson2026a,

title = {ProstNFound+: a prospective study using medical foundation models for prostate cancer detection},

author = {Paul FR Wilson and Mohamed Harmanani and Minh Nguyen Nhat To and Amoon Jamzad and Tarek Elghareb and Zhuoxin Guo and Adam Kinnaird and Brian Wodlinger and Purang Abolmaesumi and Parvin Mousavi},

year  = {2026},

date = {2026-01-01},

journal = {International Journal of Computer Assisted Radiology and Surgery},

volume = {21},

number = {4},

pages = {693-702},

publisher = {Springer International Publishing},

abstract = {Purpose 

Medical foundation models (FMs) offer a path to build high-performance diagnostic systems. However, their application to prostate cancer (PCa) detection from micro-ultrasound (US) remains untested in clinical settings. We present ProstNFound+, an adaptation of FMs for PCa detection from US, along with its first prospective validation. 

Methods 

ProstNFound+ incorporates a medical FM, adapter tuning, and a custom prompt encoder that embeds PCa-specific clinical biomarkers. The model generates a cancer heatmap and a risk score for clinically significant PCa. Following training on multicenter retrospective data, the model is prospectively evaluated on data acquired five years later from a new clinical site. Model predictions are benchmarked against standard clinical scoring protocols (PRI-MUS and PI-RADS). 

Results 

ProstNFound+ shows strong generalization to the prospective data, with no …},

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}

@article{wilson2026,

title = {Compass: Prostate Cancer Detection Needs Multi-View Context},

author = {Paul FR Wilson and Mohamed Harmanani and Zhuoxin Guo and Obed K Dzikunu and Hannes Cash and Adam Kinnaird and Brian Wodlinger and Purang Abolmaesumi and Parvin Mousavi},

year  = {2026},

date = {2026-01-01},

journal = {arXiv preprint arXiv:2607.06919},

abstract = {Artificial intelligence (AI) analysis of micro-ultrasound (US) has shown promise for prostate cancer (PCa) detection. However, most existing AI methods focus on the analysis of single US images in isolation. By contrast, expert US readers typically assess a full recorded video study, which provides three-dimensional context, to improve PCa detection compared to single-frame analysis. Inspired by this clinical workflow, we propose Compass, a novel AI methodology which models a US study as a stream of 2D images. Compass jointly integrates rotational sweep videos of the prostate with US frames acquired at the moment of biopsy, and performs evidence aggregation across the study using a transformer conditioned on the probe's rotational angle. Finally, a decoder head predicts frame-level and study-level risk scores for the patient. The model is trained and evaluated using a multi-center clinical trial dataset of US studies, including continuous rotational scans of the prostate and videos captured during biopsy acquisition. We compare the proposed method to baseline AI methods from the literature and to risk scores provided by clinical experts. Our framework shows strong performance, highlighting the value of multi-view context for US PCa detection, and providing a potentially powerful tool to complement human expertise in US-based PCa diagnosis. Our code is available at: https://github.com/mharmanani/Compass.},

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}

@article{willis2026,

title = {GUIDE-US: grade-informed unpaired distillation of encoder knowledge from histopathology to micro-ultrasound},

author = {Emma Willis and Tarek Elghareb and Paul FR Wilson and Minh Nguyen Nhat To and Mohammad Mahdi Abootorabi and Amoon Jamzad and Brian Wodlinger and Parvin Mousavi and Purang Abolmaesumi},

year  = {2026},

date = {2026-01-01},

journal = {arXiv preprint arXiv:2602.19005},

abstract = {Purpose 

Non-invasive grading of prostate cancer (PCa) from micro-ultrasound (micro-US) could expedite triage and guide biopsies toward the most aggressive regions, yet current models struggle to infer tissue micro-structure at coarse imaging resolutions. 

Methods 

We introduce an unpaired histopathology knowledge-distillation strategy that trains a micro-US encoder to emulate the embedding distribution of a pretrained histopathology foundation model, conditioned on International Society of Urological Pathology (ISUP) grades. Training requires no patient-level pairing or image registration, and histopathology inputs are not used at inference. 

Results 

Compared to the current state of the art, our approach increases sensitivity to clinically significant PCa (csPCa) at 60% specificity by 3.5% and improves overall sensitivity at 60% specificity by 1.2%. 

Conclusion 

By enabling earlier and more dependable cancer risk stratification solely from imaging, our method advances clinical feasibility. Source code will be publicly released upon publication.},

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@article{to2026,

title = {Shift happens: a fairness-oriented framework for medical classification under hidden bias},

author = {Minh Nguyen Nhat To and Diane Kim and Mohamed Harmanani and Paul FR Wilson and Fahimeh Fooladgar and Samira Sojoudi and Amoon Jamzad and Sherif Abdalla and Teresa Tsang and Christina Luong and Silvia Chang and Peter Black and Robert Siemens and Michael Leveridge and Rahul G Krishnan and Parvin Mousavi and Purang Abolmaesumi},

year  = {2026},

date = {2026-01-01},

journal = {International Journal of Computer Assisted Radiology and Surgery},

pages = {1-8},

publisher = {Springer International Publishing},

abstract = {Purpose 

Many medical AI models perform unevenly across patient groups because they learn shortcuts from biased data. These hidden biases make models less reliable and less fair in real-world use. This work aims to develop a system that remains accurate and fair across different patient subpopulations, even when those groups are not explicitly labeled. 

Methods 

We introduce DPE-Former, a model that combines prototype-based learning with transformer attention. The system trains several complementary classifiers on balanced subsets of data, each capturing different aspects of the population. A transformer module then learns how to combine its outputs in an adaptive way, helping the model make more balanced decisions across unseen or minority groups. 

Results 

Across diverse datasets, including prostate ultrasound, skin lesion images, and cardiac patient records, DPE-Former achieved higher accuracy on …},

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pubstate = {published},

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}

@article{srikanthan2026,

title = {Do Foundation Models See Biology? Evaluating Attention Coherence with Spatial Transcriptomics in Glioblastoma},

author = {Dilakshan Srikanthan and Amoon Jamzad and Paul Wilson and Nooshin Maghsoodi and Robert Policelli and Gabor Fichtinger and John F Rudan and Parvin Mousavi},

year  = {2026},

date = {2026-01-01},

journal = {arXiv preprint arXiv:2606.04764},

abstract = {Whether attention maps from pathology foundation models capture genuine biology remains unknown, yet this question is critical for clinical trust and regulatory approval. We propose a spatial transcriptomics-based framework for orthogonal, hypothesis-free evaluation of attention and apply it to five pathology foundation models (CONCH v1.5, UNI v2, Virchow2, GigaPath, H-Optimus-1) and a ResNet50 baseline. Using attention-based multiple instance learning, we train single-task and multi-task models to predict five molecular alterations in glioblastoma on the CPTAC cohort, validate on an independent TCGA cohort, and evaluate biological coherence of attention maps against 87 transcriptional signatures using co-registered Visium spatial transcriptomics data from 18 samples. Internally, no single encoder dominates across all tasks, and external validation inverts internal performance rankings. Attention maps show a five-fold enrichment gradient from pathways (Cohen's d=0.329) to individual genes (d=0.055), indicating that attention captures emergent multi-gene transcriptional programs rather than individual molecular events. Spatially smooth attention maps do not imply biological coherence, and different encoders attend to distinct biological compartments. Our framework provides objective, quantitative assessment of what foundation models learn from histopathology, moving the field beyond qualitative saliency map review.},

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@article{radcliffe2026,

title = {Advancing Prosthetic Care Access on the Thailand–Burma Border Through Open-Source Technology},

author = {Olivia Radcliffe and Aung Tin Tun and Nyein Chan Aung and Wunn Lei Thwe and Laura Connolly and Tamas Ungi and Kanchana Thornton and Colleen Davison and Eva Purkey and Parvin Mousavi and Gabor Fichtinger},

year  = {2026},

date = {2026-01-01},

journal = {IEEE pulse},

volume = {16},

number = {6},

pages = {64-70},

publisher = {IEEE},

abstract = {The ongoing civil war in Myanmar (Burma) has severely disrupted the country’s health care system, forcing widespread displacement and creating a critical shortage of medical care and prosthetic devices for refugees and migrants along the Thai border. Prohibitive costs, resource shortages, and movement constraints severely limit access to essential health care and functional prosthetics in this region. This article details a collaboration between Queen’s University and the Burma Children Medical Fund (BCMF), a nongovernmental organization (NGO) that established a 3-D-printed prosthetic program in 2019 using open-source technology. Since 2023, this collaboration has facilitated a three-year student mobility program, sending researchers to Thailand to enhance BCMF’s existing initiative by addressing technical limitations and improving design autonomy. Using the development of a short transradial (below …},

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@article{nassar2026,

title = {A Dataset and Benchmarks for Atrial Fibrillation Detection from Electrocardiograms of Intensive Care Unit Patients},

author = {Sarah Nassar and Nooshin Maghsoodi and Sophia Mannina and Shamel Addas and Stephanie Sibley and Gabor Fichtinger and David Pichora and David Maslove and Purang Abolmaesumi and Parvin Mousavi},

year  = {2026},

date = {2026-01-01},

journal = {IEEE Transactions on Biomedical Engineering},

publisher = {IEEE},

abstract = {Objective 

Atrial fibrillation (AF) is the most common cardiac arrhythmia experienced by intensive care unit (ICU) patients and can cause adverse health effects. In this study, we publish a labelled ICU dataset and bench marks for AF detection. 

Methods 

We compared machine learning models across three data-driven artificial intelligence (AI) approaches: feature-based classifiers, deep learning (DL), and ECG foundation models (FMs). This comparison addresses a critical gap in the literature and aims to pinpoint which AI approach is best for high-performing AF detection. Electrocardiograms (ECGs) from a Canadian ICU and the 2021 PhysioNet/Computing in Cardiology Challenge were used to conduct the experiments. Multiple training configurations were tested, ranging from zero-shot in ference to transfer learning. 

Results 

Across both datasets, ECG FMs generally performed best, followed by DL, then feature …},

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pubstate = {published},

tppubtype = {article}

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@article{maghsoodi2026a,

title = {Domain Knowledge is Power: Leveraging Physiological Priors for Self-Supervised Representation Learning in Electrocardiography},

author = {Nooshin Maghsoodi and Sarah Nassar and Paul FR Wilson and Minh Nguyen Nhat To and Sophia Mannina and Shamel Addas and Stephanie Sibley and David Pichora and David Maslove and Purang Abolmaesumi and Parvin Mousavi},

year  = {2026},

date = {2026-01-01},

journal = {IEEE Transactions on Biomedical Engineering},

publisher = {IEEE},

abstract = {Objective 

Electrocardiograms (ECGs) play a crucial role in diagnosing heart conditions; however, the effectiveness of artificial intelligence (AI)-based ECG analysis is often hindered by the limited availability of labeled data. Self-supervised learning (SSL) can address this by leveraging large-scale unlabeled data. We introduce PhysioCLR (Physiology-aware Contrastive Learning Representation for ECG), a physiology-aware contrastive learning framework that incorporates domain-specific priors to enhance the generalizability and clinical relevance of ECG-based arrhythmia classification. 

Methods 

During pretraining, PhysioCLR learns to bring together embeddings of samples that share similar clinically relevant features while pushing apart those that are dissimilar. Unlike existing methods, our method integrates ECG physiological similarity cues into contrastive learning, promoting the learning of clinically …},

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@article{maghsoodi2026,

title = {Agent-Guided Relational Concept Discovery: Toward Interpretable Surgical Margin Assessment},

author = {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},

year  = {2026},

date = {2026-01-01},

journal = {arXiv preprint arXiv:2607.21437},

abstract = {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.},

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@article{liu2026,

title = {Integrating Foundation Models into MassVision for Mass Spectrometry Imaging},

author = {Sophia Liu and Amoon Jamzad and Mohammad Farahmand and Parvin Mousavi},

year  = {2026},

date = {2026-01-01},

journal = {Inquiry@ Queen's Undergraduate Research Conference Proceedings},

volume = {20},

number = {2},

abstract = {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.},

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pubstate = {published},

tppubtype = {article}

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@article{jamzad2026,

title = {P3CA: Encoder-Agnostic Interpretation of Vision Foundation Model Embeddings via Spatial Probing},

author = {Amoon Jamzad and Dilakshan Srikanthan and Faranak Akbarifar and Nooshin Maghsoodi and Parvin Mousavi},

year  = {2026},

date = {2026-01-01},

journal = {arXiv preprint arXiv:2608.10131},

abstract = {Vision foundation models are increasingly used as reusable encoders in medical image computing, yet their high-dimensional spatial embeddings are difficult to inspect beyond downstream task performance or global dimensionality reduction. We propose position-prompted PCA (P3CA), an encoder-agnostic method for local probing of channel-rich spatial tensors. Given a user-selected spatial prompt, P3CA estimates the feature normalization and dominant covariance directions within that region, then applies the resulting projection to the full tensor to visualize where locally informative directions are expressed. This produces a region-conditioned representation lens without modifying the encoder, retraining, or requiring task-specific labels. We implement P3CA in EmbedVision, an interactive 3D Slicer-based workflow, and evaluate it across natural images, colorectal pathology foundation-model embeddings, and spatial transcriptomic tensors. Across these settings, prompted projections reveal local structure suppressed by global PCA, improve prompt-matched pathology discrimination from frozen three-dimensional projections, and support comparison between learned and measured spatial representations.},

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tppubtype = {article}

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@article{harmanani2026,

title = {Vision-Language Models Encode Clinical Guidelines for Concept-Based Medical Reasoning},

author = {Mohamed Harmanani and Bining Long and Zhuoxin Guo and Paul FR Wilson and Amirhossein Sabour and Minh Nguyen Nhat To and Gabor Fichtinger and Purang Abolmaesumi and Parvin Mousavi},

year  = {2026},

date = {2026-01-01},

journal = {arXiv preprint arXiv:2603.08921},

abstract = {Concept Bottleneck Models (CBMs) are a prominent framework for interpretable AI that map learned visual features to a set of meaningful concepts for task-specific downstream predictions. Their sequential structure enhances transparency by connecting model predictions to the underlying concepts that support them. In medical imaging, where transparency is essential, CBMs offer an appealing foundation for explainable model design. However, discrete concept representations often overlook broader clinical context such as diagnostic guidelines and expert heuristics, reducing reliability in complex cases. We propose MedCBR, a concept-based reasoning framework that integrates clinical guidelines with vision-language and reasoning models. Labeled clinical descriptors are transformed into guideline-conformant text, and a concept-based model is trained with a multitask objective combining multimodal contrastive alignment, concept supervision, and diagnostic classification to jointly ground image features, concepts, and pathology. A reasoning model then converts these predictions into structured clinical narratives that explain the diagnosis, emulating expert reasoning based on established guidelines. MedCBR achieves superior diagnostic and concept-level performance, with AUROCs of 94.2% on ultrasound and 84.0% on mammography. Further experiments on non-medical datasets achieve 86.1% accuracy. Our framework enhances interpretability and forms an end-to-end bridge from medical image analysis to decision-making.},

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tppubtype = {article}

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@article{dzikunu2026,

title = {Learning Prostate Anatomy at Test Time for Cancer Detection in Micro-Ultrasound},

author = {Obed Korshie Dzikunu and Mohammad Mahdi Abootorabi and Mohamed Harmanani and Paul FR Wilson and Emma Willis and Ferdinand Luger and Adam Kinnaird and Brian Wodlinger and Parvin Mousavi and Purang Abolmaesumi},

year  = {2026},

date = {2026-01-01},

journal = {arXiv preprint arXiv:2608.20557},

abstract = {Domain shift across clinical centers using different imaging hardware or acquisition protocols remains a fundamental barrier to deploying deep learning models for prostate cancer (PCa) detection. Existing test-time adaptation (TTA) methods address distribution shift through entropy minimization or augmentation-based self-supervision, correcting for statistical differences in image appearance but ignoring the anatomical structure of the target domain. We propose ANT, a segmentation-guided TTA framework that adapts a pretrained cancer detection encoder to the target domain by solving an auxiliary prostate segmentation task at test time, supervised by pseudo-masks from a frozen pretrained segmentation network. By aligning encoder representations to prostate anatomy in the target domain, ANT corrects domain-specific feature drift while preserving cancer-discriminative structure. The model was trained on 693 patients imaged with an earlier-generation micro-ultrasound scanner in a multi-center clinical trial, and evaluated on 118 patients acquired with a newer-generation system across two centers in another clinical trial. Under a leave-one-center-out protocol with identical evaluation conditions across all methods, ANT improves mean AUC by 2.9% and 3.6% at the biopsy-core and patient levels, respectively, over no adaptation, outperforming TTA baselines. Code is available at: https://github.com/ObedDzik/ant.git.},

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@article{connolly2026,

title = {No cancer left behind: a testbed and demonstration of concept for photoacoustic tumor bed inspection},

author = {Laura Connolly and Hyunwoo Song and Keshuai Xu and Anton Deguet and Simon Leonard and Gabor Fichtinger and Parvin Mousavi and Russell H Taylor and Emad Boctor},

year  = {2026},

date = {2026-01-01},

journal = {Computer Assisted Surgery},

volume = {31},

number = {1},

pages = {2604123},

publisher = {Taylor & Francis},

abstract = {Cancer resection surgery is unsuccessful if tumor tissue is left behind in the surgical cavity. Identifying the residual cancer requires additional imaging or postoperative histological analysis. Photoacoustic imaging can be used to image both the surface and depths of the resection cavity; however, its performance hinges on consistent probe placement and stable acoustic and optical coupling. As intra-cavity deployment of photoacoustic imaging is largely uncharted, several potential embodiments warrant rigorous investigation. We address this need with an open-source robotic testbed for intraoperative tumor-bed inspection using photoacoustic imaging. The platform integrates the da Vinci Research Kit, depth imaging, and electromagnetic tracking to automate cavity scanning and maintain repeatable probe trajectories. Using tissue-mimicking phantoms, we (i) demonstrate a novel imaging embodiment for …},

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tppubtype = {article}

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@article{barr2026,

title = {Local LLMs as cooperative agents for low-cost surgical navigation support},

author = {Colton Barr and Colin Galvin and Amirali Azimi and Sarah Frisken and Steve Pieper and Gabor Fichtinger and Alexandra Golby and Parvin Mousavi},

year  = {2026},

date = {2026-01-01},

journal = {International Journal of Computer Assisted Radiology and Surgery},

pages = {1-8},

publisher = {Springer International Publishing},

abstract = {Purpose 

Computer-integrated surgical navigation systems are often run in the OR with the assistance of a technician for controlling the user interface and advising on technical details of the system. In lower-resource healthcare settings, limited access to additional OR staff and technical training for operating navigation systems can represent a barrier to sustainably deploying a low-cost surgical navigation solution. Recent advancements in locally deployable large language models (LLMs) have improved their ability to answer technical questions based on source materials and safely perform limited tasks on behalf of a user. 

Methods 

The objective of this paper is to explore the feasibility of using a network of local LLM-based agents to act as a natural language interface for a low-cost surgical navigation system, facilitating hands-free manipulation of the user interface and providing documentation-grounded technical …},

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pubstate = {published},

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@article{akbarifar2026,

title = {Reducing robotic upper-limb assessment time while maintaining precision: a time series foundation model approach},

author = {Faranak Akbarifar and Nooshin Maghsoodi and Sean P Dukelow and Stephen H Scott and Parvin Mousavi},

year  = {2026},

date = {2026-01-01},

journal = {Journal of NeuroEngineering and Rehabilitation},

publisher = {BioMed Central},

abstract = {Purpose 

Visually Guided Reaching (VGR) on the Kinarm robot yields sensitive kinematic biomarkers but requires 40–64 reaches, imposing time and fatigue burdens. We evaluate whether time series foundation models can replace unrecorded trials from an early subset of reaches while preserving agreement with full-session estimates of standard Kinarm parameters. 

Methods 

We analyzed VGR speed signals from 461 stroke and 599 control participants across 4- and 8-target reaching protocols. We withheld all but the first 8 or 16 reaching trials and used ARIMA, MOMENT, and Chronos models, fine-tuned on 70% of participants, to forecast synthetic trials. We recomputed four kinematic features of reaching (reaction time, movement time, posture speed, max speed) on combined recorded plus forecasted trials and compared to full-length references using ICC(2,1). 

Results 

Chronos forecasts increased ICC values for …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}