Get our free extension to see links to code for papers anywhere online!

Add to Chrome

Add to Firefox

Picture for Yuanhong Chen

Yuanhong Chen

Knowledge Distillation to Ensemble Global and Interpretable Prototype-Based Mammogram Classification Models

Sep 26, 2022
Chong Wang, Yuanhong Chen, Yuyuan Liu, Yu Tian, Fengbei Liu, Davis J. McCarthy, Michael Elliott, Helen Frazer, Gustavo Carneiro

* 11 pages, 4 figures, conference. Springer, Cham, 2022

Access Paper or Ask Questions

Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection

Mar 23, 2022
Yu Tian, Guansong Pang, Fengbei Liu, Yuyuan Liu, Chong Wang, Yuanhong Chen, Johan W Verjans, Gustavo Carneiro

Figure 1 for Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection

Figure 2 for Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection

Figure 3 for Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection

Figure 4 for Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection

* Technical report, 10 pages, 3 figures

Access Paper or Ask Questions

Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked Autoencoder

Mar 22, 2022
Yu Tian, Guansong Pang, Yuyuan Liu, Chong Wang, Yuanhong Chen, Fengbei Liu, Rajvinder Singh, Johan W Verjans, Gustavo Carneiro

Figure 1 for Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked Autoencoder

Figure 2 for Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked Autoencoder

Figure 3 for Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked Autoencoder

Figure 4 for Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked Autoencoder

* Technical report, 11 pages, 3 figures

Access Paper or Ask Questions