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Pain intensity estimation based on a spatial transformation and attention CNN.

Xuwu Xin¹, Xiaoyan Lin¹, Shengfu Yang²

¹The Second Affiliated Hospital of Shantou University Medical College, Shantou, China.

|August 22, 2020

Summary

This study introduces a novel attention network with spatial transformation for automated pain intensity estimation from facial images. The method effectively reduces background noise and focuses on relevant facial regions, outperforming existing state-of-the-art approaches.

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Area of Science:

Computer Vision
Medical Image Analysis
Artificial Intelligence in Healthcare

Background:

Automated facial analysis models for disease detection are emerging in smart healthcare.
Existing models often overlook background interference and regional facial variations.
Accurate pain intensity estimation from facial cues remains a challenge.

Purpose of the Study:

To develop an advanced model for accurate pain intensity estimation using facial images.
To address the limitations of current models regarding background noise and regional focus.
To improve automated facial analysis for healthcare applications.

Main Methods:

Proposed an end-to-end attention network incorporating spatial transformation.
Utilized a spatial transformation network to mitigate background interference.
Employed an attention mechanism to weigh critical facial regions adaptively.
Classified pain levels using a convolutional neural network (CNN) with a Softmax function.

Main Results:

The proposed method demonstrated superior performance in pain intensity estimation.
Spatial transformation and attention mechanisms significantly enhanced estimation accuracy.
The model outperformed baseline CNNs and existing state-of-the-art methods on the UNBC-McMaster shoulder pain dataset.

Conclusions:

The novel attention network with spatial transformation is effective for pain intensity estimation.
The approach offers a significant advancement in automated facial analysis for healthcare.
This method holds promise for objective pain assessment in clinical settings.