5 citations · 11 across the 8 of their papers we have counts for
13 papers
Interpretable Machine Learning for Weather and Climate Prediction: A Survey
Ruyi Yang, Jingyu Hu, Zihao Li +6
Advanced machine learning models have recently achieved high predictive accuracy for weather and climate prediction. However, these complex models often lack inherent transparency…
Explanations of Classifiers Enhance Medical Image Segmentation via End-to-end Pre-training
Jiamin Chen, Xuhong Li, Yanwu Xu +2
Medical image segmentation aims to identify and locate abnormal structures in medical images, such as chest radiographs, using deep neural networks. These networks require a large…
Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspective
Haoyi Xiong, Xuhong Li, Xiaofei Zhang +5
Given the complexity and lack of transparency in deep neural networks (DNNs), extensive efforts have been made to make these systems more interpretable or explain their behaviors i…
CUPre: Cross-domain Unsupervised Pre-training for Few-Shot Cell Segmentation
Weibin Liao, Xuhong Li, Qingzhong Wang +3
While pre-training on object detection tasks, such as Common Objects in Contexts (COCO) [1], could significantly boost the performance of cell segmentation, it still consumes on ma…
MUSCLE: Multi-task Self-supervised Continual Learning to Pre-train Deep Models for X-ray Images of Multiple Body Parts
Weibin Liao, Haoyi Xiong, Qingzhong Wang +6
While self-supervised learning (SSL) algorithms have been widely used to pre-train deep models, few efforts [11] have been done to improve representation learning of X-ray image an…
Robust Cross-Modal Knowledge Distillation for Unconstrained Videos
Wenke Xia, Xingjian Li, Andong Deng +3
Cross-modal distillation has been widely used to transfer knowledge across different modalities, enriching the representation of the target unimodal one. Recent studies highly rela…