79 citations · 341 across the 40 of their papers we have counts for
7 papers · 1 filter
Aggregative Self-Supervised Feature Learning from a Limited Sample
Jiuwen Zhu, Yuexiang Li, S. Kevin Zhou
Self-supervised learning (SSL) is an efficient approach that addresses the issue of limited training data and annotation shortage. The key part in SSL is its proxy task that define…
MI^2GAN: Generative Adversarial Network for Medical Image Domain Adaptation using Mutual Information Constraint
Xinpeng Xie, Jiawei Chen, Yuexiang Li +3
Domain shift between medical images from multicentres is still an open question for the community, which degrades the generalization performance of deep learning models. Generative…
Instance-aware Self-supervised Learning for Nuclei Segmentation
Xinpeng Xie, Jiawei Chen, Yuexiang Li +3
Due to the wide existence and large morphological variances of nuclei, accurate nuclei instance segmentation is still one of the most challenging tasks in computational pathology.…
Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-Supervised Medical Image Segmentation
Yuexiang Li, Jiawei Chen, Xinpeng Xie +2
Witnessing the success of deep learning neural networks in natural image processing, an increasing number of studies have been proposed to develop deep-learning-based frameworks fo…
Revisiting Rubik's Cube: Self-supervised Learning with Volume-wise Transformation for 3D Medical Image Segmentation
Xing Tao, Yuexiang Li, Wenhui Zhou +2
Deep learning highly relies on the quantity of annotated data. However, the annotations for 3D volumetric medical data require experienced physicians to spend hours or even days fo…
Embedding Task Knowledge into 3D Neural Networks via Self-supervised Learning
Jiuwen Zhu, Yuexiang Li, Yifan Hu +1
Deep learning highly relies on the amount of annotated data. However, annotating medical images is extremely laborious and expensive. To this end, self-supervised learning (SSL), a…
Generative Adversarial Networks for Video-to-Video Domain Adaptation
Jiawei Chen, Yuexiang Li, Kai Ma +1
Endoscopic videos from multicentres often have different imaging conditions, e.g., color and illumination, which make the models trained on one domain usually fail to generalize we…