49 citations · 57 across the 9 of their papers we have counts for
10 papers
Classifier-head Informed Feature Masking and Prototype-based Logit Smoothing for Out-of-Distribution Detection
Zhuohao Sun, Yiqiao Qiu, Zhijun Tan +2
Out-of-distribution (OOD) detection is essential when deploying neural networks in the real world. One main challenge is that neural networks often make overconfident predictions o…
Adapter Learning in Pretrained Feature Extractor for Continual Learning of Diseases
Wentao Zhang, Yujun Huang, Tong Zhang +3
Currently intelligent diagnosis systems lack the ability of continually learning to diagnose new diseases once deployed, under the condition of preserving old disease knowledge. In…
PAMI: partition input and aggregate outputs for model interpretation
Wei Shi, Wentao Zhang, Weishi Zheng +1
There is an increasing demand for interpretation of model predictions especially in high-risk applications. Various visualization approaches have been proposed to estimate the part…
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…
Learning Discriminative Representation via Metric Learning for Imbalanced Medical Image Classification
Chenghua Zeng, Huijuan Lu, Kanghao Chen +2
Data imbalance between common and rare diseases during model training often causes intelligent diagnosis systems to have biased predictions towards common diseases. The state-of-th…
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…