6 citations · 8 across the 2 of their papers we have counts for
6 papers
Robust breast cancer detection in mammography and digital breast tomosynthesis using annotation-efficient deep learning approach
William Lotter, Abdul Rahman Diab, Bryan Haslam +10
Breast cancer remains a global challenge, causing over 1 million deaths globally in 2018. To achieve earlier breast cancer detection, screening x-ray mammography is recommended by…
Validation of a deep learning mammography model in a population with low screening rates
Kevin Wu, Eric Wu, Yaping Wu +4
A key promise of AI applications in healthcare is in increasing access to quality medical care in under-served populations and emerging markets. However, deep learning models are o…
Learning Cross-Modal Deep Representations for Multi-Modal MR Image Segmentation
Cheng Li, Hui Sun, Zaiyi Liu +3
Multi-modal magnetic resonance imaging (MRI) is essential in clinics for comprehensive diagnosis and surgical planning. Nevertheless, the segmentation of multi-modal MR images tend…
CLCI-Net: Cross-Level fusion and Context Inference Networks for Lesion Segmentation of Chronic Stroke
Hao Yang, Weijian Huang, Kehan Qi +5
Segmenting stroke lesions from T1-weighted MR images is of great value for large-scale stroke rehabilitation neuroimaging analyses. Nevertheless, there are great challenges with th…
X-Net: Brain Stroke Lesion Segmentation Based on Depthwise Separable Convolution and Long-range Dependencies
Kehan Qi, Hao Yang, Cheng Li +4
The morbidity of brain stroke increased rapidly in the past few years. To help specialists in lesion measurements and treatment planning, automatic segmentation methods are critica…
AUNet: Attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms
Hui Sun, Cheng Li, Boqiang Liu +3
Mammography is one of the most commonly applied tools for early breast cancer screening. Automatic segmentation of breast masses in mammograms is essential but challenging due to t…