381 citations · 717 across the 31 of their papers we have counts for
11 papers · 1 filter
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…
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…
Deep Learning for Detection and Localization of B-Lines in Lung Ultrasound
Ruben T. Lucassen, Mohammad H. Jafari, Nicole M. Duggan +18
Lung ultrasound (LUS) is an important imaging modality used by emergency physicians to assess pulmonary congestion at the patient bedside. B-line artifacts in LUS videos are key fi…
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…
EchoGNN: Explainable Ejection Fraction Estimation with Graph Neural Networks
Masoud Mokhtari, Teresa Tsang, Purang Abolmaesumi +1
Ejection fraction (EF) is a key indicator of cardiac function, allowing identification of patients prone to heart dysfunctions such as heart failure. EF is estimated from cardiac u…
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…
A Study into Echocardiography View Conversion
Amir H. Abdi, Mohammad H. Jafari, Sidney Fels +2
Transthoracic echo is one of the most common means of cardiac studies in the clinical routines. During the echo exam, the sonographer captures a set of standard cross sections (ech…
GAN-enhanced Conditional Echocardiogram Generation
Amir H. Abdi, Teresa Tsang, Purang Abolmaesumi
Echocardiography (echo) is a common means of evaluating cardiac conditions. Due to the label scarcity, semi-supervised paradigms in automated echo analysis are getting traction. On…
Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation
Alireza Mehrtash, William M. Wells, Clare M. Tempany +2
Fully convolutional neural networks (FCNs), and in particular U-Nets, have achieved state-of-the-art results in semantic segmentation for numerous medical imaging applications. Mor…