Machine Learning

Our Machine Learning Team is mainly devoted to：

Developing autoML technology under scenarios such as low data and weak tags to improve the operational efficiency and interpretability of AI and develop a reliable AI system;
Conducting research on uncertainty probabilistic inference technology and combining with various deep learning models to develop a highly reliable medical diagnosis AI system.

As a young research team, we not only published our research results at important academic conferences such as MICCAI, IJCNN, ICIP, etc., but also cooperated with many well-known domestic hospitals in the field of AI for medical diagnosis and quickly applied research results to clinical practice.

Publication

【ISBI 2021】UNCERTAINTY-GUIDED ROBUST TRAINING FOR MEDICAL IMAGE SEGMENTATION
【IJCNN 2021 (Accepted)】Model Performance Inspection of Deep Neural Networks by Decomposing Bayesian Uncertainty Estimates
【IJCNN 2021 (Accepted)】Layerwise Approximate Inference for Bayesian Uncertainty Estimates on Deep Neural Networks
【MICCAI 2020】An Effective Data Refinement Approach for Upper Gastrointestinal Anatomy Recognition
【ICIP 2020】Loss Rescaling by Uncertainty Inference for Single-stage Object Detection
【IJCNN 2020】A Layer-wise Adversarial Training Approach to Improve Adversarial Robustness

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Machine Learning

Publication