4 citations · 11 across the 11 of their papers we have counts for
7 papers · 1 filter
Preserving Cardiac Integrity: A Topology-Infused Approach to Whole Heart Segmentation
Chenyu Zhang, Wenxue Guan, Xiaodan Xing +1
Whole heart segmentation (WHS) supports cardiovascular disease (CVD) diagnosis, disease monitoring, treatment planning, and prognosis. Deep learning has become the most widely used…
The Beauty or the Beast: Which Aspect of Synthetic Medical Images Deserves Our Focus?
Xiaodan Xing, Yang Nan, Federico Felder +2
Training medical AI algorithms requires large volumes of accurately labeled datasets, which are difficult to obtain in the real world. Synthetic images generated from deep generati…
Less is More: Unsupervised Mask-guided Annotated CT Image Synthesis with Minimum Manual Segmentations
Xiaodan Xing, Giorgos Papanastasiou, Simon Walsh +1
As a pragmatic data augmentation tool, data synthesis has generally returned dividends in performance for deep learning based medical image analysis. However, generating correspond…
Hierarchical Perception Adversarial Learning Framework for Compressed Sensing MRI
Zhifan Gao, Yifeng Guo, Jiajing Zhang +2
The long acquisition time has limited the accessibility of magnetic resonance imaging (MRI) because it leads to patient discomfort and motion artifacts. Although several MRI techni…
Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation
Yang Nan, Javier Del Ser, Zeyu Tang +7
Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases, while its manual delineation is unduly burdensome. To alleviate this time-consuming a…
Unsupervised Tissue Segmentation via Deep Constrained Gaussian Network
Yang Nan, Peng Tang, Guyue Zhang +5
Tissue segmentation is the mainstay of pathological examination, whereas the manual delineation is unduly burdensome. To assist this time-consuming and subjective manual step, rese…
CS: A Controllable and Simultaneous Synthesizer of Images and Annotations with Minimal Human Intervention
Xiaodan Xing, Jiahao Huang, Yang Nan +5
The destitution of image data and corresponding expert annotations limit the training capacities of AI diagnostic models and potentially inhibit their performance. To address such…