2 citations · 3 across the 6 of their papers we have counts for
7 papers
Concept-wise Attention for Fine-grained Concept Bottleneck Models
Minghong Zhong, Guoshuai Zou, Kanghao Chen +2
Recently impressive performance has been achieved in Concept Bottleneck Models (CBM) by utilizing the image-text alignment learned by a large pre-trained vision-language model (i.e…
Hierarchical Vision-Language Learning for Medical Out-of-Distribution Detection
Runhe Lai, Xinhua Lu, Kanghao Chen +3
In trustworthy medical diagnosis systems, integrating out-of-distribution (OOD) detection aims to identify unknown diseases in samples, thereby mitigating the risk of misdiagnosis.…
Augmenting Continual Learning of Diseases with LLM-Generated Visual Concepts
Jiantao Tan, Peixian Ma, Kanghao Chen +2
Continual learning is essential for medical image classification systems to adapt to dynamically evolving clinical environments. The integration of multimodal information can signi…
FA: Forced Prompt Learning of Vision-Language Models for Out-of-Distribution Detection
Xinhua Lu, Runhe Lai, Yanqi Wu +3
Pre-trained vision-language models (VLMs) have advanced out-of-distribution (OOD) detection recently. However, existing CLIP-based methods often focus on learning OOD-related knowl…
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…