2 citations · 3 across the 3 of their papers we have counts for
3 papers
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…
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…
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…