基于显著性信息的Fit CutMix数据增强算法在医学影像上的应用

doi:10.11959/j.issn.2096-6652.202307

Abstract

Abstract:

Deep convolutional neural network is one of the mainstream algorithms in the field of image classification, but its training requires a large number of labeled data, which leads to over fitting on small datasets such as Alzheimer's medical images.Data augmentation can increase the amount of training data, and CutMix data augmentation algorithm has been widely used recently.However, the augmented images generated by the CutMix series methods often ignore the significant area of the original image, and the design of the label of the augmented image takes only single factor into consideration.In order to solve these problems, the Fit CutMix data augmentation algorithm was proposed.Firstly, the region replacement strategy based on the transfer of saliency extreme value was used to generate augmented samples, so as to concentrate the regions with high saliency value in the source samples and target samples.Secondly, the area and saliency information of the source samples and the target samples were combined to assign the augmented sample label, which provided effective supervision information for the convolutional neural network.The experimental results showed that when Fit CutMix was used in ResNet50 to diagnose Alzheimer's disease, the accuracy was 96.6%, which was about 7% higher than that of directly using ResNet50, and at least 3% higher than that of applying existing methods.Therefore, the Fit CutMix data augmentation algorithm can effectively improve the recognition accuracy of deep convolutional neural network for medical images.

Key words: data augmentation, convolutional neural network, Alzheimer's disease, saliency detection

CLC Number:

TP399

Xinhuan LUO, Yixuan WANG, Wei LI, et al. Application of Fit CutMix data augmentation algorithm based on saliency information in medical images[J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(1): 58-68.

Figures/Tables 11

算法	Acc	Sen	Spe	Pre	F1值	AUC
ResNet18	91.86%	93.33%	90.86%	87.51%	90.32	92.10
ResNet18+Aug	93.90%	95.00%	93.71%	91.50%	93.08	94.36
VGG11	90.51%	93.33%	88.57%	84.84%	88.88	90.95
VGG11+Aug	91.53%	95.83%	88.57%	85.19%	90.20	92.20
VGG19	84.41%	90.00%	80.57%	76.37%	82.27	85.29
VGG19+Aug	85.76%	95.00%	79.43%	76.15%	84.47	87.21
DensNet121	92.54%	91.67%	93.14%	90.26%	90.93	92.40
DensNet121+Aug	93.22%	94.17%	92.57%	90.07%	91.94	93.37
ResNet50	89.83%	91.67%	88.57%	85.56%	88.19	90.12
$R e s N e t 50 + A u g$	$96 . 61 %$	$95 . 83 %$	$97 . 14 %$	$95 . 93 %$	$95 . 87$	$96 . 49$

算法	Acc	Sen	Pre-AD	Pre-MCI	Pre-NC	F1值
ResNet18	85.91%	86.94%	84.71%	83.73%	84.57%	86.58
$R e s N e t 18 + A u g$	$87 . 50 %$	$88 . 45 %$	$86 . 28 %$	$85 . 98 %$	$85 . 23 %$	$88 . 06$
VGG11	78.41%	78.98%	76.23%	74.61%	76.72%	78.51
VGG11+Aug	81.59%	82.58%	78.45%	77.98%	78.10%	82.33
VGG19	79.09%	79.98%	77.09%	76.01%	78.78%	79.43
VGG19+Aug	82.95%	83.57%	80.42%	80.12%	80.53%	83.41
DensNet121	82.50%	82.99%	79.23%	78.67%	80.71%	83.27
DensNet121+Aug	85.00%	85.98%	82.22%	82.20%	81.39%	85.59
ResNet50	80.68%	81.20%	76.80%	75.38%	76.49%	81.50
ResNet50+Aug	85.23%	85.96%	79.92%	78.95%	80.41%	86.22

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分组	AD	MCI	NC
数量/人	116	99	174
年龄/岁	74.6 ± 7.5	73.4 ± 16.0	75.5 ± 6.1

	源图像—NC样本	目标图像—AD样本	增强图像
原图
AD类别的Grad-CAM图
NC类别的Grad-CAM图

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Application of Fit CutMix data augmentation algorithm based on saliency information in medical images

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