5 citations · 18 across the 14 of their papers we have counts for
14 papers
P3CA: Encoder-Agnostic Interpretation of Vision Foundation Model Embeddings via Spatial Probing
Amoon Jamzad, Dilakshan Srikanthan, Faranak Akbarifar +2
Vision foundation models are increasingly used as reusable encoders in medical image computing, yet their high-dimensional spatial embeddings are difficult to inspect beyond downst…
Agent-Guided Relational Concept Discovery: Toward Interpretable Surgical Margin Assessment
Nooshin Maghsoodi, Amoon Jamzad, Robert Policelli +11
Deep learning models can effectively use Rapid Evaporative Ionization Mass Spectrometry (REIMS) data for surgical margin assessment. However, their clinical adoption remains challe…
Do Foundation Models See Biology? Evaluating Attention Coherence with Spatial Transcriptomics in Glioblastoma
Dilakshan Srikanthan, Amoon Jamzad, Paul Wilson +5
Whether attention maps from pathology foundation models capture genuine biology remains unknown, yet this question is critical for clinical trust and regulatory approval. We propos…
GUIDE-US: Grade-Informed Unpaired Distillation of Encoder Knowledge from Histopathology to Micro-UltraSound
Emma Willis, Tarek Elghareb, Paul F. R. Wilson +6
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 mod…
ProstNFound+: A Prospective Study using Medical Foundation Models for Prostate Cancer Detection
Paul F. R. Wilson, Mohamed Harmanani, Minh Nguyen Nhat To +7
Purpose: Medical foundation models (FMs) offer a path to build high-performance diagnostic systems. However, their application to prostate cancer (PCa) detection from micro-ultraso…
FACT: Foundation Model for Assessing Cancer Tissue Margins with Mass Spectrometry
Mohammad Farahmand, Amoon Jamzad, Fahimeh Fooladgar +7
Purpose: Accurately classifying tissue margins during cancer surgeries is crucial for ensuring complete tumor removal. Rapid Evaporative Ionization Mass Spectrometry (REIMS), a too…
TRUSWorthy: Toward Clinically Applicable Deep Learning for Confident Detection of Prostate Cancer in Micro-Ultrasound
Mohamed Harmanani, Paul F. R. Wilson, Minh Nguyen Nhat To +6
While deep learning methods have shown great promise in improving the effectiveness of prostate cancer (PCa) diagnosis by detecting suspicious lesions from trans-rectal ultrasound…
Cinepro: Robust Training of Foundation Models for Cancer Detection in Prostate Ultrasound Cineloops
Mohamed Harmanani, Amoon Jamzad, Minh Nguyen Nhat To +11
Prostate cancer (PCa) detection using deep learning (DL) models has shown potential for enhancing real-time guidance during biopsies. However, prostate ultrasound images lack pixel…
Calibrated Diverse Ensemble Entropy Minimization for Robust Test-Time Adaptation in Prostate Cancer Detection
Mahdi Gilany, Mohamed Harmanani, Paul Wilson +6
High resolution micro-ultrasound has demonstrated promise in real-time prostate cancer detection, with deep learning becoming a prominent tool for learning complex tissue propertie…
Benchmarking Image Transformers for Prostate Cancer Detection from Ultrasound Data
Mohamed Harmanani, Paul F. R. Wilson, Fahimeh Fooladgar +6
PURPOSE: Deep learning methods for classifying prostate cancer (PCa) in ultrasound images typically employ convolutional networks (CNNs) to detect cancer in small regions of intere…
Domain Transfer Through Image-to-Image Translation for Uncertainty-Aware Prostate Cancer Classification
Meng Zhou, Amoon Jamzad, Jason Izard +3
Prostate Cancer (PCa) is a prevalent disease among men, and multi-parametric MRIs offer a non-invasive method for its detection. While MRI-based deep learning solutions have shown…
TRUSformer: Improving Prostate Cancer Detection from Micro-Ultrasound Using Attention and Self-Supervision
Mahdi Gilany, Paul Wilson, Andrea Perera-Ortega +6
A large body of previous machine learning methods for ultrasound-based prostate cancer detection classify small regions of interest (ROIs) of ultrasound signals that lie within a l…
Self-Supervised Learning with Limited Labeled Data for Prostate Cancer Detection in High Frequency Ultrasound
Paul F. R. Wilson, Mahdi Gilany, Amoon Jamzad +5
Deep learning-based analysis of high-frequency, high-resolution micro-ultrasound data shows great promise for prostate cancer detection. Previous approaches to analysis of ultrasou…
Towards Confident Detection of Prostate Cancer using High Resolution Micro-ultrasound
Mahdi Gilany, Paul Wilson, Amoon Jamzad +5
MOTIVATION: Detection of prostate cancer during transrectal ultrasound-guided biopsy is challenging. The highly heterogeneous appearance of cancer, presence of ultrasound artefacts…