no code implementations • CVPR 2020 • Shuxin Wang, Shilei Cao, Dong Wei, Renzhen Wang, Kai Ma, Liansheng Wang, Deyu Meng, Yefeng Zheng
We introduce a one-shot segmentation method to alleviate the burden of manual annotation for medical images.
no code implementations • 8 Aug 2020 • Renzhen Wang, Kaiqin Hu, Yanwen Zhu, Jun Shu, Qian Zhao, Deyu Meng
We further design a modulator network to guide the generation of the modulation parameters, and such a meta-learner can be readily adapted to train the classification network on other long-tailed datasets.
1 code implementation • 17 Dec 2020 • Huai Chen, Jieyu Li, Renzhen Wang, YiJie Huang, Fanrui Meng, Deyu Meng, Qing Peng, Lisheng Wang
However, the commonly applied supervised representation learning methods require a large amount of annotated data, and unsupervised discriminative representation learning distinguishes different images by learning a global feature, both of which are not suitable for localized medical image analysis tasks.
no code implementations • 27 Jun 2021 • Quanziang Wang, Renzhen Wang, Yuexiang Li, Kai Ma, Yefeng Zheng, Deyu Meng
Location information is proven to benefit the deep learning models on capturing the manifold structure of target objects, and accordingly boosts the accuracy of medical image segmentation.
1 code implementation • 21 Aug 2021 • Huai Chen, Renzhen Wang, Xiuying Wang, Jieyu Li, Qu Fang, Hui Li, Jianhao Bai, Qing Peng, Deyu Meng, Lisheng Wang
To address this challenge, in this paper, we propose a general unsupervised representation learning framework, named local discrimination (LD), to learn local discriminative features for medical images by closely embedding semantically similar pixels and identifying regions of similar structures across different images.
1 code implementation • 4 Dec 2021 • Renzhen Wang, De Cai, Kaiwen Xiao, Xixi Jia, Xiao Han, Deyu Meng
Existing methods commonly address hierarchical classification by decoupling it into a series of multi-class classification tasks.
no code implementations • 31 Dec 2021 • Quanziang Wang, Renzhen Wang, Yuexiang Li, Dong Wei, Kai Ma, Yefeng Zheng, Deyu Meng
Continual learning is a promising machine learning paradigm to learn new tasks while retaining previously learned knowledge over streaming training data.
1 code implementation • 28 Jul 2022 • Renzhen Wang, Xixi Jia, Quanziang Wang, Yichen Wu, Deyu Meng
The core idea is to automatically assimilate the training bias caused by class imbalance via the bias adaptive classifier, which is composed of a novel bias attractor and the original linear classifier.
1 code implementation • ICCV 2023 • Quanziang Wang, Renzhen Wang, Yichen Wu, Xixi Jia, Deyu Meng
Online continual learning (CL) aims to learn new knowledge and consolidate previously learned knowledge from non-stationary data streams.