240 citations · 312 across the 11 of their papers we have counts for
11 papers
Class Attention to Regions of Lesion for Imbalanced Medical Image Recognition
Jia-Xin Zhuang, Jiabin Cai, Jianguo Zhang +2
Automated medical image classification is the key component in intelligent diagnosis systems. However, most medical image datasets contain plenty of samples of common diseases and…
Adaptively Integrated Knowledge Distillation and Prediction Uncertainty for Continual Learning
Kanghao Chen, Sijia Liu, Ruixuan Wang +1
Current deep learning models often suffer from catastrophic forgetting of old knowledge when continually learning new knowledge. Existing strategies to alleviate this issue often f…
PCCT: Progressive Class-Center Triplet Loss for Imbalanced Medical Image Classification
Kanghao Chen, Weixian Lei, Rong Zhang +3
Imbalanced training data is a significant challenge for medical image classification. In this study, we propose a novel Progressive Class-Center Triplet (PCCT) framework to allevia…
Task-oriented Self-supervised Learning for Anomaly Detection in Electroencephalography
Yaojia Zheng, Zhouwu Liu, Rong Mo +3
Accurate automated analysis of electroencephalography (EEG) would largely help clinicians effectively monitor and diagnose patients with various brain diseases. Compared to supervi…
Continual Learning with Bayesian Model based on a Fixed Pre-trained Feature Extractor
Yang Yang, Zhiying Cui, Junjie Xu +3
Deep learning has shown its human-level performance in various applications. However, current deep learning models are characterised by catastrophic forgetting of old knowledge whe…
SATS: Self-Attention Transfer for Continual Semantic Segmentation
Yiqiao Qiu, Yixing Shen, Zhuohao Sun +4
Continually learning to segment more and more types of image regions is a desired capability for many intelligent systems. However, such continual semantic segmentation suffers fro…
Understanding of Kernels in CNN Models by Suppressing Irrelevant Visual Features in Images
Jia-Xin Zhuang, Wanying Tao, Jianfei Xing +3
Deep learning models have shown their superior performance in various vision tasks. However, the lack of precisely interpreting kernels in convolutional neural networks (CNNs) is b…
Discriminative Distillation to Reduce Class Confusion in Continual Learning
Changhong Zhong, Zhiying Cui, Ruixuan Wang +1
Successful continual learning of new knowledge would enable intelligent systems to recognize more and more classes of objects. However, current intelligent systems often fail to co…
Preserving Earlier Knowledge in Continual Learning with the Help of All Previous Feature Extractors
Zhuoyun Li, Changhong Zhong, Sijia Liu +2
Continual learning of new knowledge over time is one desirable capability for intelligent systems to recognize more and more classes of objects. Without or with very limited amount…
Towards Unbiased COVID-19 Lesion Localisation and Segmentation via Weakly Supervised Learning
Yang Yang, Jiancong Chen, Ruixuan Wang +5
Despite tremendous efforts, it is very challenging to generate a robust model to assist in the accurate quantification assessment of COVID-19 on chest CT images. Due to the nature…
Fully Convolutional Network Ensembles for White Matter Hyperintensities Segmentation in MR Images
Hongwei Li, Gongfa Jiang, Jianguo Zhang +4
White matter hyperintensities (WMH) are commonly found in the brains of healthy elderly individuals and have been associated with various neurological and geriatric disorders. In t…