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Shi Ying

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2 papers

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EAAI Journal 2026 Journal Article

A lightweight and real-time surgical action detection framework using multi-contextual and decoupled representations

Siming Zheng
A.S.M. Sharifuzzaman Sagar
Yu Chen
Jun Hoong Chan
Zehao Yu
Shi Ying
Jianfeng Lu

Accurate detection of surgical actions in minimally invasive procedures is a critical step toward developing intelligent operative assistance systems. In this work, we propose Surgical You Only Look Once detector (Surg-YOLO), an efficient and high-precision surgical action detection framework built upon the YOLO version 11 (YOLOv11) architecture, specifically optimized for the spatio-temporal complexities of surgical environments. Surg-YOLO integrates three key architectural innovations: the Enhanced Spatial Pyramid Pooling-Fast (ESPPF) module for capturing rich multi-scale spatial features; the Spatio-Temporal Multi-scale Context Aggregation Module (ST-MCAM), which enhances temporal reasoning and contextual awareness across frames; and the Decoupled Dual-Branch Prediction Head (DDPH) for independently refining classification and localization tasks. Extensive experiments on a large-scale surgical action dataset demonstrate that Surg-YOLO significantly outperforms existing baseline models, achieving superior detection accuracy across multiple evaluation thresholds. Qualitative visualizations further validate the model’s ability to localize subtle and concurrent surgical actions with high precision. These results highlight Surg-YOLO’s potential as a reliable solution for real-time surgical action detection.

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AAAI Conference 2021 Conference Paper

Diffusion Network Inference from Partial Observations

Ting Gan
Keqi Han
Hao Huang
Shi Ying
Yunjun Gao
Zongpeng Li

To infer the structure of a diffusion network from observed diffusion results, existing approaches customarily assume that observed data are complete and contain the final infection status of each node, as well as precise timestamps of node infections. Due to high cost and uncertainties in the monitoring of node infections, exact timestamps are often unavailable in practice, and even the final infection statuses of nodes are sometimes missing. In this work, we study how to carry out diffusion network inference without infection timestamps, using only partial observations of the final infection statuses of nodes. To this end, we iteratively infer the structure of the target diffusion network with observed data and imputed values for missing data, and learn the most likely infection transmission probabilities between nodes w. r. t. current inferred structure, which then help us update the imputation of missing data in turn. Extensive experimental results on both synthetic and real-world networks show that our approach can properly handle missing data and accurately uncover diffusion network structures.

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