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Hi 👋! Here is Meirui Jiang, final year Ph.D. student in the CSE, CUHK.

My research centers around the intersection of machine learning and healthcare, with a particular focus on medical image analysis. I am driven by a strong desire to enhance the applicability of machine learning algorithms, with a focus on their robustness, efficiency, generalizability, reliability, and privacy. In particular, I am passionate about leveraging large distributed datasets to empower real-world applications in the healthcare domain. By exploring this interdisciplinary realm, I aim to contribute to advancing machine learning techniques and their transformative impact on healthcare practices.

I am always open to engaging in discussions and collaborations. Please feel free to reach out to me via email :)

Please learn more about me from my personal website.

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Meirui Jiang's Projects

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[MICCAI2023] Client-Level Differential Privacy via Adaptive Intermediary in Federated Medical Imaging

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Data Shapley: Equitable Valuation of Data for Machine Learning

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[ICLR'21] FedBN: Federated Learning on Non-IID Features via Local Batch Normalization

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[CVPR'21] FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space

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[npj Digital Medicine'21] Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study. (Nature publishing group)

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Flower - A Friendly Federated Learning Framework

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[AAAI'22] HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images

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[MICCAI'22] Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalance

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PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more

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