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PCAN: Pixel-wise classification and attention network for thoracic disease classification and weakly supervised

Xiongfeng Zhu¹, Shumao Pang¹, Xiaoxuan Zhang¹

¹School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, 510515, China.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society

|October 29, 2022

Summary

This study introduces a novel Pixel-wise Classification and Attention Network (PCAN) for enhanced chest X-ray disease classification and localization. PCAN improves interpretability and accuracy in diagnosing thoracic conditions.

Keywords:

Chest X-ray Disease classification Weakly supervised localization

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Chest X-ray (CXR) is crucial for thoracic disease diagnosis.
Current classification networks struggle with small, varied-sized, or misplaced lesions.
Global average pooling limits performance in complex CXR analyses.

Purpose of the Study:

To develop a new network for simultaneous disease classification and weakly supervised localization in CXRs.
To enhance interpretability in CXR disease diagnosis.
To address limitations of existing methods in detecting diverse lesion characteristics.

Main Methods:

Proposed Pixel-wise Classification and Attention Network (PCAN).
PCAN utilizes a backbone for feature extraction, a pixel-wise classification branch (pc-branch), and a pixel-wise attention branch (pa-branch).
Combines pixel-wise diagnoses with attention weights for lesion localization and image-wise diagnosis.

Main Results:

PCAN effectively performs simultaneous disease classification and weakly supervised localization.
The pc-branch aids in detecting small lesions.
The pa-branch adaptively focuses on relevant regions for accurate classification.
Demonstrated effectiveness on ChestX-ray14 and CheXpert datasets.

Conclusions:

PCAN offers improved performance and interpretability for thoracic disease diagnosis.
The network shows significant potential for clinical application in medical imaging.
Weakly supervised localization aids in understanding disease presentation on CXRs.