41 citations · 115 across the 15 of their papers we have counts for
15 papers
SAFE-Diff: Scale-Aware Attention and Feature-Dispersive Diffusion with Uncertainty Estimation for Contrast-Enhanced Breast MRI Synthesis
Tianyu Zhang, Xinglong Liang, Jarek van Dijk +13
Synthesizing high fidelity contrast enhanced MRI is clinically valuable for safer and more efficient breast cancer screening, yet remains challenging due to complex lesion textures…
Foundation Models in Medical Imaging: A Review and Outlook
Vivien van Veldhuizen, Vanessa Botha, Chunyao Lu +10
Foundation models (FMs) are changing the way medical images are analyzed by learning from large collections of unlabeled data. Instead of relying on manually annotated examples, FM…
Ordinal Learning: Longitudinal Attention Alignment Model for Predicting Time to Future Breast Cancer Events from Mammograms
Xin Wang, Tao Tan, Yuan Gao +9
Precision breast cancer (BC) risk assessment is crucial for developing individualized screening and prevention. Despite the promising potential of recent mammogram (MG) based deep…
To deform or not: treatment-aware longitudinal registration for breast DCE-MRI during neoadjuvant chemotherapy via unsupervised keypoints detection
Luyi Han, Tao Tan, Tianyu Zhang +7
Clinicians compare breast DCE-MRI after neoadjuvant chemotherapy (NAC) with pre-treatment scans to evaluate the response to NAC. Clinical evidence supports that accurate longitudin…
Improving Lesion Volume Measurements on Digital Mammograms
Nikita Moriakov, Jim Peters, Ritse Mann +4
Lesion volume is an important predictor for prognosis in breast cancer. We make a step towards a more accurate lesion volume measurement on digital mammograms by developing a model…
Synthesis of Contrast-Enhanced Breast MRI Using Multi-b-Value DWI-based Hierarchical Fusion Network with Attention Mechanism
Tianyu Zhang, Luyi Han, Anna D'Angelo +7
Magnetic resonance imaging (MRI) is the most sensitive technique for breast cancer detection among current clinical imaging modalities. Contrast-enhanced MRI (CE-MRI) provides supe…
IMPORTANT-Net: Integrated MRI Multi-Parameter Reinforcement Fusion Generator with Attention Network for Synthesizing Absent Data
Tianyu Zhang, Tao Tan, Luyi Han +5
Magnetic resonance imaging (MRI) is highly sensitive for lesion detection in the breasts. Sequences obtained with different settings can capture the specific characteristics of les…
Synthesis-based Imaging-Differentiation Representation Learning for Multi-Sequence 3D/4D MRI
Luyi Han, Tao Tan, Tianyu Zhang +5
Multi-sequence MRIs can be necessary for reliable diagnosis in clinical practice due to the complimentary information within sequences. However, redundant information exists across…
FlowNet-PET: Unsupervised Learning to Perform Respiratory Motion Correction in PET Imaging
Teaghan O'Briain, Carlos Uribe, Kwang Moo Yi +3
To correct for respiratory motion in PET imaging, an interpretable and unsupervised deep learning technique, FlowNet-PET, was constructed. The network was trained to predict the op…
Automatic Breast Lesion Detection in Ultrafast DCE-MRI Using Deep Learning
Fazael Ayatollahi, Shahriar B. Shokouhi, Ritse M. Mann +1
Purpose: We propose a deep learning-based computer-aided detection (CADe) method to detect breast lesions in ultrafast DCE-MRI sequences. This method uses both the three-dimensiona…
Deep Learning-based Initialization of Iterative Reconstruction for Breast Tomosynthesis
Koen Michielsen, Nikita Moriakov, Jonas Teuwen +1
Reconstruction of digital breast tomosynthesis is a challenging problem due to the limited angle data available in such systems. Due to memory limitations, deep learning-based meth…
Deep learning reconstruction of digital breast tomosynthesis images for accurate breast density and patient-specific radiation dose estimation
Jonas Teuwen, Nikita Moriakov, Christian Fedon +7
The two-dimensional nature of mammography makes estimation of the overall breast density challenging, and estimation of the true patient-specific radiation dose impossible. Digital…
Vendor-independent soft tissue lesion detection using weakly supervised and unsupervised adversarial domain adaptation
Joris van Vugt, Elena Marchiori, Ritse Mann +3
Computer-aided detection aims to improve breast cancer screening programs by helping radiologists to evaluate digital mammography (DM) exams. DM exams are generated by devices from…
Deep Learning Framework for Digital Breast Tomosynthesis Reconstruction
Nikita Moriakov, Koen Michielsen, Jonas Adler +3
Digital breast tomosynthesis is rapidly replacing digital mammography as the basic x-ray technique for evaluation of the breasts. However, the sparse sampling and limited angular r…
Automated soft tissue lesion detection and segmentation in digital mammography using a u-net deep learning network
Timothy de Moor, Alejandro Rodriguez-Ruiz, Albert Gubern Mérida +2
Computer-aided detection or decision support systems aim to improve breast cancer screening programs by helping radiologists to evaluate digital mammography (DM) exams. Commonly su…