Chen (Cherise) Chen
@cherise-chen.bsky.social
Lecturer in Computer Vision at University of Sheffield | Robust&Trustworthy AI for healthcare
https://cherise215.github.io/
https://cherise215.github.io/
Work done by my PhD student: Sijie Li, co-supervised with Prof. Jungong Han. @shefcmi.bsky.social
August 4, 2025 at 6:41 PM
Work done by my PhD student: Sijie Li, co-supervised with Prof. Jungong Han. @shefcmi.bsky.social
Reposted by Chen (Cherise) Chen
In particular, we'd like to thank our speakers: Prof Laoise Mcnamara, Dr Dawn Walker, Dr Xiancheng Yu, Prof John Rasmussen, Dr @elleneb.bsky.social, Prof Julia Weinstein, Dr Neil Stewart, Prof Gwendolen Reilly, @proflemaitre.bsky.social, Dr @cherise-chen.bsky.social & Prof Robin Purshouse.
July 7, 2025 at 11:27 AM
In particular, we'd like to thank our speakers: Prof Laoise Mcnamara, Dr Dawn Walker, Dr Xiancheng Yu, Prof John Rasmussen, Dr @elleneb.bsky.social, Prof Julia Weinstein, Dr Neil Stewart, Prof Gwendolen Reilly, @proflemaitre.bsky.social, Dr @cherise-chen.bsky.social & Prof Robin Purshouse.
Our approach makes AI-driven ECG analysis more reliable for real-world applications. Let's work on robust, interpretable AI for better human-AI collaboration in healthcare together!
Code: github.com/cherise215/L...
Paper: doi.org/10.1109/tbda...
All open access!
Code: github.com/cherise215/L...
Paper: doi.org/10.1109/tbda...
All open access!
GitHub - cherise215/LLM-ECG-Dual-Attention
Contribute to cherise215/LLM-ECG-Dual-Attention development by creating an account on GitHub.
github.com
February 13, 2025 at 1:01 PM
Our approach makes AI-driven ECG analysis more reliable for real-world applications. Let's work on robust, interpretable AI for better human-AI collaboration in healthcare together!
Code: github.com/cherise215/L...
Paper: doi.org/10.1109/tbda...
All open access!
Code: github.com/cherise215/L...
Paper: doi.org/10.1109/tbda...
All open access!
🔹 Key Insights:
✅ Robust Dual Attention Design with Explainability – Captures cross-lead interactions and local temporal dynamics, improving accuracy and clinical relevance!
✅ LLM-Informed Pretraining- Uses a LLM pre-trained on ECG reports to align text and waveform data, improving generalizability
✅ Robust Dual Attention Design with Explainability – Captures cross-lead interactions and local temporal dynamics, improving accuracy and clinical relevance!
✅ LLM-Informed Pretraining- Uses a LLM pre-trained on ECG reports to align text and waveform data, improving generalizability
February 13, 2025 at 1:01 PM
🔹 Key Insights:
✅ Robust Dual Attention Design with Explainability – Captures cross-lead interactions and local temporal dynamics, improving accuracy and clinical relevance!
✅ LLM-Informed Pretraining- Uses a LLM pre-trained on ECG reports to align text and waveform data, improving generalizability
✅ Robust Dual Attention Design with Explainability – Captures cross-lead interactions and local temporal dynamics, improving accuracy and clinical relevance!
✅ LLM-Informed Pretraining- Uses a LLM pre-trained on ECG reports to align text and waveform data, improving generalizability