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Creators/Authors contains: "Xue, Yexiang"

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  1. ; ; ; ; ; ; ; ; ; ( , Military Medicine)
    ABSTRACT Introduction

    Between 5% and 20% of all combat-related casualties are attributed to burn wounds. A decrease in the mortality rate of burns by about 36% can be achieved with early treatment, but this is contingent upon accurate characterization of the burn. Precise burn injury classification is recognized as a crucial aspect of the medical artificial intelligence (AI) field. An autonomous AI system designed to analyze multiple characteristics of burns using modalities including ultrasound and RGB images is described.

     Materials and Methods

    A two-part dataset is created for the training and validation of the AI: in vivo B-mode ultrasound scans collected from porcine subjects (10,085 frames), and RGB images manually collected from web sources (338 images). The framework in use leverages an explanation system to corroborate and integrate burn expert’s knowledge, suggesting new features and ensuring the validity of the model. Through the utilization of this framework, it is discovered that B-mode ultrasound classifiers can be enhanced by supplying textural features. More specifically, it is confirmed that statistical texture features extracted from ultrasound frames can increase the accuracy of the burn depth classifier.

     Results

    The system, with all included features selected using explainable AI, is capable of classifying burn depth with accuracy and F1 average above 80%. Additionally, the segmentation module has been found capable of segmenting with a mean global accuracy greater than 84%, and a mean intersection-over-union score over 0.74.

     Conclusions

    This work demonstrates the feasibility of accurate and automated burn characterization for AI and indicates that these systems can be improved with additional features when a human expert is combined with explainable AI. This is demonstrated on real data (human for segmentation and porcine for depth classification) and establishes the groundwork for further deep-learning thrusts in the area of burn analysis.

     
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  2. ; ; ; ; ( , Proc. of The 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS))
    Free, publicly-accessible full text available June 1, 2024
  3. ; ; ( , Proc. Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI))
    Accepted Manuscript Full Text Available
  4. ; ; ; ( , Journal of machine learning research)
    Accepted Manuscript Full Text Available
  5. ; ( , Proceedings of the 2022 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD))
    Accepted Manuscript Full Text Available
  6. ; ; ; ( , Journal of machine learning research)
    Accepted Manuscript Full Text Available
  7. ; ; ; ; ( , Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC))
    Accepted Manuscript Full Text Available
  8. ; ( , Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence (UAI))
    Accepted Manuscript Full Text Available
  9. ; ; ; ; ; ; ; ; ; ; et al ( , IEEE Transactions on Medical Robotics and Bionics)
    Full Text Available 
  10. ; ; ; ( , Journal of machine learning research)
    Many real-world structured prediction problems need machine learning to capture data distribution and constraint reasoning to ensure structure validity. Nevertheless, constrained structured prediction is still limited in real-world applications because of the lack of tools to bridge constraint satisfaction and machine learning. In this paper, we propose COnstraint REasoning embedded Structured Prediction (Core-Sp), a scalable constraint reasoning and machine learning integrated approach for learning over structured domains. We propose to embed decision diagrams, a popular constraint reasoning tool, as a fully-differentiable module into deep neural networks for structured prediction. We also propose an iterative search algorithm to automate the searching process of the best Core-Sp structure. We evaluate Core-Sp on three applications: vehicle dispatching service planning, if-then program synthesis, and text2SQL generation. The proposed Core-Sp module demonstrates superior performance over state-of-the-art approaches in all three applications. The structures generated with Core-Sp satisfy 100% of the constraints when using exact decision diagrams. In addition, Core-Sp boosts learning performance by reducing the modeling space via constraint satisfaction. 
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    Accepted Manuscript Full Text Available