44 citations · 65 across the 9 of their papers we have counts for
9 papers
Domain Adaptation Meets Zero-Shot Learning: An Annotation-Efficient Approach to Multi-Modality Medical Image Segmentation
Cheng Bian, Chenglang Yuan, Kai Ma +3
Due to the lack of properly annotated medical data, exploring the generalization capability of the deep model is becoming a public concern. Zero-shot learning (ZSL) has emerged in…
Multi-Anchor Active Domain Adaptation for Semantic Segmentation
Munan Ning, Donghuan Lu, Dong Wei +5
Unsupervised domain adaption has proven to be an effective approach for alleviating the intensive workload of manual annotation by aligning the synthetic source-domain data and the…
A New Bidirectional Unsupervised Domain Adaptation Segmentation Framework
Munan Ning, Cheng Bian, Dong Wei +5
Domain shift happens in cross-domain scenarios commonly because of the wide gaps between different domains: when applying a deep learning model well-trained in one domain to anothe…
TR-GAN: Topology Ranking GAN with Triplet Loss for Retinal Artery/Vein Classification
Wenting Chen, Shuang Yu, Junde Wu +5
Retinal artery/vein (A/V) classification lays the foundation for the quantitative analysis of retinal vessels, which is associated with potential risks of various cardiovascular an…
Difficulty-aware Glaucoma Classification with Multi-Rater Consensus Modeling
Shuang Yu, Hong-Yu Zhou, Kai Ma +4
Medical images are generally labeled by multiple experts before the final ground-truth labels are determined. Consensus or disagreement among experts regarding individual images re…
Leveraging Undiagnosed Data for Glaucoma Classification with Teacher-Student Learning
Junde Wu, Shuang Yu, Wenting Chen +5
Recently, deep learning has been adopted to the glaucoma classification task with performance comparable to that of human experts. However, a well trained deep learning model deman…