8 citations · 14 across the 4 of their papers we have counts for
5 papers · 1 filter
Scalable Semi-supervised Landmark Localization for X-ray Images using Few-shot Deep Adaptive Graph
Xiao-Yun Zhou, Bolin Lai, Weijian Li +12
Landmark localization plays an important role in medical image analysis. Learning based methods, including CNN and GCN, have demonstrated the state-of-the-art performance. However,…
Hetero-Modal Learning and Expansive Consistency Constraints for Semi-Supervised Detection from Multi-Sequence Data
Bolin Lai, Yuhsuan Wu, Xiao-Yun Zhou +7
Lesion detection serves a critical role in early diagnosis and has been well explored in recent years due to methodological advancesand increased data availability. However, the hi…
SegAttnGAN: Text to Image Generation with Segmentation Attention
Yuchuan Gou, Qiancheng Wu, Minghao Li +2
In this paper, we propose a novel generative network (SegAttnGAN) that utilizes additional segmentation information for the text-to-image synthesis task. As the segmentation data i…
Prior-aware Neural Network for Partially-Supervised Multi-Organ Segmentation
Yuyin Zhou, Zhe Li, Song Bai +5
Accurate multi-organ abdominal CT segmentation is essential to many clinical applications such as computer-aided intervention. As data annotation requires massive human labor from…
Abnormal Chest X-ray Identification With Generative Adversarial One-Class Classifier
Yuxing Tang, Youbao Tang, Mei Han +2
Being one of the most common diagnostic imaging tests, chest radiography requires timely reporting of potential findings in the images. In this paper, we propose an end-to-end arch…