2 citations · 8 across the 6 of their papers we have counts for
4 papers · 1 filter
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…