18 citations · 64 across the 22 of their papers we have counts for
6 papers · 1 filter
Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding
Liyan Sun, Chenxin Li, Xinghao Ding +3
Despite deep convolutional neural networks achieved impressive progress in medical image computing and analysis, its paradigm of supervised learning demands a large number of annot…
Adaptive noise imitation for image denoising
Huangxing Lin, Yihong Zhuang, Yue Huang +4
The effectiveness of existing denoising algorithms typically relies on accurate pre-defined noise statistics or plenty of paired data, which limits their practicality. In this work…
A Teacher-Student Framework for Semi-supervised Medical Image Segmentation From Mixed Supervision
Liyan Sun, Jianxiong Wu, Xinghao Ding +3
Standard segmentation of medical images based on full-supervised convolutional networks demands accurate dense annotations. Such learning framework is built on laborious manual ann…
Hard Class Rectification for Domain Adaptation
Yunlong Zhang, Changxing Jing, Huangxing Lin +4
Domain adaptation (DA) aims to transfer knowledge from a label-rich and related domain (source domain) to a label-scare domain (target domain). Pseudo-labeling has recently been wi…
Multi-Task Neural Networks with Spatial Activation for Retinal Vessel Segmentation and Artery/Vein Classification
Wenao Ma, Shuang Yu, Kai Ma +3
Retinal artery/vein (A/V) classification plays a critical role in the clinical biomarker study of how various systemic and cardiovascular diseases affect the retinal vessels. Conve…
Harmonizing Transferability and Discriminability for Adapting Object Detectors
Chaoqi Chen, Zebiao Zheng, Xinghao Ding +2
Recent advances in adaptive object detection have achieved compelling results in virtue of adversarial feature adaptation to mitigate the distributional shifts along the detection…