6 papers · 1 filter
DpDNet: An Dual-Prompt-Driven Network for Universal PET-CT Segmentation
Xinglong Liang, Jiaju Huang, Luyi Han +7
PET-CT lesion segmentation is challenging due to noise sensitivity, small and variable lesion morphology, and interference from physiological high-metabolic signals. Current mainst…
Prompt Mechanisms in Medical Imaging: A Comprehensive Survey
Hao Yang, Xinlong Liang, Zhang Li +12
Deep learning offers transformative potential in medical imaging, yet its clinical adoption is frequently hampered by challenges such as data scarcity, distribution shifts, and the…
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…
Comparing DWI image quality of deep-learning-reconstructed EPI with RESOLVE in breast lesions at 3.0T: a pilot study
Marialena I. Tsarouchi, Antonio Portaluri, Marnix Maas +1
The challenging spatial resolution of DWI could be addressed by deep learning based image reconstruction, by reducing noise without increasing acquisition time. To compare the imag…
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…
Fine-Grained Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma Segmentation
Luyi Han, Tao Tan, Ritse Mann
The domain adaptation approach has gained significant acceptance in transferring styles across various vendors and centers, along with filling the gaps in modalities. However, mult…