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Detecting cerebral microbleeds via deep learning with features enhancement by reusing ground truth.

Tianfu Li1, Yan Zou2, Pengfei Bai3

  • 1Guangdong Provincial Key Laboratory of Optical Information Materials and Technology & Institute of Electronic Paper Displays, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China.

Computer Methods and Programs in Biomedicine
|April 8, 2021
PubMed
Summary

Feature enhancement technology improves deep learning models for detecting cerebral microbleeds (CMBs) in brain MRI scans. This method boosts precision and mean average precision (mAP), aiding in diagnosing conditions like stroke and dementia.

Keywords:
Cerebral microbleedsConvolutional neural networkDeep learningFeature enhancementSSD

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neurology

Background:

  • Cerebral microbleeds (CMBs) are indicative of small vascular diseases, crucial for diagnosing stroke and dementia.
  • Manual detection of CMBs is labor-intensive and prone to errors.
  • Deep learning algorithms offer significant potential for automated CMB detection.

Purpose of the Study:

  • To introduce and evaluate a feature enhancement technology for improving CMB detection using deep learning.
  • To emphasize meaningful features and suppress irrelevant ones in feature maps for better model optimization.

Main Methods:

  • Feature enhancement was applied to the SSD-512 and SSD-300 algorithms, utilizing a VGG architecture pre-trained on ImageNet.
  • The technique selectively enhances informative feature map intervals while suppressing noise.
  • Models were trained and tested on SWAN brain MRI image sequences.

Main Results:

  • The feature enhancement method improved precision by 3.3% and mAP by 2.3% for SSD-512.
  • SSD-300 demonstrated an improved mAP of 2.0% with the enhancement.
  • Precision gains of 2.8% and 7.4% were observed for ResNet-34 and MobileNet, respectively.

Conclusions:

  • Feature enhancement is an effective technique for improving deep learning-based CMB detection.
  • The proposed method offers a valuable tool for enhancing the performance of CMB detection models.