5 papers
Bias-constrained multimodal intelligence for equitable and reliable clinical AI
Cheng Li, Weijian Huang, Jiarun Liu +6
The integration of medical imaging and clinical text has enabled the emergence of generalist artificial intelligence (AI) systems for healthcare. However, pervasive biases, such as…
Cross-Sequence Semi-Supervised Learning for Multi-Parametric MRI-Based Visual Pathway Delineation
Alou Diakite, Cheng Li, Lei Xie +5
Accurately delineating the visual pathway (VP) is crucial for understanding the human visual system and diagnosing related disorders. Exploring multi-parametric MR imaging data has…
Automating Vessel Segmentation in the Heart and Brain: A Trend to Develop Multi-Modality and Label-Efficient Deep Learning Techniques
Nazik Elsayed, Yousuf Babiker M. Osman, Cheng Li +2
Cardio-cerebrovascular diseases are the leading causes of mortality worldwide, whose accurate blood vessel segmentation is significant for both scientific research and clinical usa…
Modality Exchange Network for Retinogeniculate Visual Pathway Segmentation
Hua Han, Cheng Li, Lei Xie +3
Accurate segmentation of the retinogeniculate visual pathway (RGVP) aids in the diagnosis and treatment of visual disorders by identifying disruptions or abnormalities within the p…
LESEN: Label-Efficient deep learning for Multi-parametric MRI-based Visual Pathway Segmentation
Alou Diakite, Cheng Li, Lei Xie +3
Recent research has shown the potential of deep learning in multi-parametric MRI-based visual pathway (VP) segmentation. However, obtaining labeled data for training is laborious a…