most citedDomain Adaptation Meets Zero-Shot Learning: An Annotation-Efficient Approach to Multi-Modality Medical Image Segmentation

44 citations

5 papers

cs.CV202212 cited

Lesion Guided Explainable Few Weak-shot Medical Report Generation

Jinghan Sun, Dong Wei, Liansheng Wang +1

Medical images are widely used in clinical practice for diagnosis. Automatically generating interpretable medical reports can reduce radiologists' burden and facilitate timely care…

cs.CV202244 cited

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…

eess.IV20201 cited

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…

cs.CV20202 cited

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…

cs.CV20202 cited

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…