85 citations · 223 across the 29 of their papers we have counts for
14 papers · 1 filter
Prostate-Specific Foundation Models for Enhanced Detection of Clinically Significant Cancer
Jeong Hoon Lee, Cynthia Xinran Li, Hassan Jahanandish +14
Accurate prostate cancer diagnosis remains challenging. Even when using MRI, radiologists exhibit low specificity and significant inter-observer variability, leading to potential d…
Registration-Enhanced Segmentation Method for Prostate Cancer in Ultrasound Images
Shengtian Sang, Hassan Jahanandish, Cynthia Xinran Li +8
Prostate cancer is a major cause of cancer-related deaths in men, where early detection greatly improves survival rates. Although MRI-TRUS fusion biopsy offers superior accuracy by…
Multimodal MRI-Ultrasound AI for Prostate Cancer Detection Outperforms Radiologist MRI Interpretation: A Multi-Center Study
Hassan Jahanandish, Shengtian Sang, Cynthia Xinran Li +6
Pre-biopsy magnetic resonance imaging (MRI) is increasingly used to target suspicious prostate lesions. This has led to artificial intelligence (AI) applications improving MRI-base…
Mask Enhanced Deeply Supervised Prostate Cancer Detection on B-mode Micro-Ultrasound
Lichun Zhang, Steve Ran Zhou, Moon Hyung Choi +14
Prostate cancer is a leading cause of cancer-related deaths among men. The recent development of high frequency, micro-ultrasound imaging offers improved resolution compared to con…
ProsDectNet: Bridging the Gap in Prostate Cancer Detection via Transrectal B-mode Ultrasound Imaging
Sulaiman Vesal, Indrani Bhattacharya, Hassan Jahanandish +8
Interpreting traditional B-mode ultrasound images can be challenging due to image artifacts (e.g., shadowing, speckle), leading to low sensitivity and limited diagnostic accuracy.…
Domain Generalization for Prostate Segmentation in Transrectal Ultrasound Images: A Multi-center Study
Sulaiman Vesal, Iani Gayo, Indrani Bhattacharya +7
Prostate biopsy and image-guided treatment procedures are often performed under the guidance of ultrasound fused with magnetic resonance images (MRI). Accurate image fusion relies…
Few-shot Unsupervised Domain Adaptation for Multi-modal Cardiac Image Segmentation
Mingxuan Gu, Sulaiman Vesal, Ronak Kosti +1
Unsupervised domain adaptation (UDA) methods intend to reduce the gap between source and target domains by using unlabeled target domain and labeled source domain data, however, in…
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Alessa Hering, Lasse Hansen, Tony C. W. Mok +50
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medi…
Spatio-temporal Multi-task Learning for Cardiac MRI Left Ventricle Quantification
Sulaiman Vesal, Mingxuan Gu, Andreas Maier +1
Quantitative assessment of cardiac left ventricle (LV) morphology is essential to assess cardiac function and improve the diagnosis of different cardiovascular diseases. In current…
Cardiac Segmentation on Late Gadolinium Enhancement MRI: A Benchmark Study from Multi-Sequence Cardiac MR Segmentation Challenge
Xiahai Zhuang, Jiahang Xu, Xinzhe Luo +20
Accurate computing, analysis and modeling of the ventricles and myocardium from medical images are important, especially in the diagnosis and treatment management for patients suff…
COPD Classification in CT Images Using a 3D Convolutional Neural Network
Jalil Ahmed, Sulaiman Vesal, Felix Durlak +4
Chronic obstructive pulmonary disease (COPD) is a lung disease that is not fully reversible and one of the leading causes of morbidity and mortality in the world. Early detection a…
Deep Learning-based Denoising of Mammographic Images using Physics-driven Data Augmentation
Dominik Eckert, Sulaiman Vesal, Ludwig Ritschl +2
Mammography is using low-energy X-rays to screen the human breast and is utilized by radiologists to detect breast cancer. Typically radiologists require a mammogram with impeccabl…
Automated Multi-sequence Cardiac MRI Segmentation Using Supervised Domain Adaptation
Sulaiman Vesal, Nishant Ravikumar, Andreas Maier
Left ventricle segmentation and morphological assessment are essential for improving diagnosis and our understanding of cardiomyopathy, which in turn is imperative for reducing ris…
Dilated deeply supervised networks for hippocampus segmentation in MRI
Lukas Folle, Sulaiman Vesal, Nishant Ravikumar +1
Tissue loss in the hippocampi has been heavily correlated with the progression of Alzheimer's Disease (AD). The shape and structure of the hippocampus are important factors in term…