Uncertainty Estimation in Deep Neural Networks for Dermoscopic Image Classification

Abstract Transfer learning from natural images is well used in deep neural networks (DNNs) for medical image classification to achieve computer-aided clinical diagnosis. Although the adversarial vulnerability of DNNs hinders practical applications owing to the high stakes of diagnosis, adversarial attacks are expected to be limited because training data — which are often required for adversarial attacks — are generally unavailable in terms of security and privacy preservation. Nevertheless, we hypothesized that adversarial attacks are also possible using natural images because pre-trained models do not change significantly after fine-tuning. We focused on three representative DNN-based medical image classification tasks (i.e., skin cancer, referable diabetic retinopathy, and pneumonia classifications) and investigated whether medical DNN models with transfer learning are vulnerable to universal adversarial perturbations (UAPs), generated using natural images. UAPs from natural images are useful for both non-targeted and targeted attacks. The performance of UAPs from natural images was significantly higher than that of random controls, although slightly lower than that of UAPs from training images. Vulnerability to UAPs from natural images was observed between different natural image datasets and between different model architectures. The use of transfer learning causes a security hole, which decreases the reliability and safety of computer-based disease diagnosis. Model training from random initialization (without transfer learning) reduced the performance of UAPs from natural images; however, it did not completely avoid vulnerability to UAPs. The vulnerability of UAPs from natural images will become a remarkable security threat.

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Compact Deep Neural Networks for Device-Based Image Classification

Mobile Cloud Visual Media Computing ◽

10.1007/978-3-319-24702-1_8 ◽

2015 ◽

pp. 201-217 ◽

Cited By ~ 4

Author(s):

Zejia Zheng ◽

Zhu Li ◽

Abhishek Nagar

Keyword(s):

Neural Networks ◽

Image Classification ◽

Deep Neural Networks

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A New Image Classification Approach via Improved MobileNet Models with Local Receptive Field Expansion in Shallow Layers

Computational Intelligence and Neuroscience ◽

10.1155/2020/8817849 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Wei Wang ◽

Yiyang Hu ◽

Ting Zou ◽

Hongmei Liu ◽

Jin Wang ◽

...

Keyword(s):

Neural Networks ◽

Computational Complexity ◽

Embedded Systems ◽

Receptive Field ◽

Image Classification ◽

Classification Accuracy ◽

Deep Neural Networks ◽

Classification Approach ◽

Dilated Convolution ◽

Convolution Filters

Because deep neural networks (DNNs) are both memory-intensive and computation-intensive, they are difficult to apply to embedded systems with limited hardware resources. Therefore, DNN models need to be compressed and accelerated. By applying depthwise separable convolutions, MobileNet can decrease the number of parameters and computational complexity with less loss of classification precision. Based on MobileNet, 3 improved MobileNet models with local receptive field expansion in shallow layers, also called Dilated-MobileNet (Dilated Convolution MobileNet) models, are proposed, in which dilated convolutions are introduced into a specific convolutional layer of the MobileNet model. Without increasing the number of parameters, dilated convolutions are used to increase the receptive field of the convolution filters to obtain better classification accuracy. The experiments were performed on the Caltech-101, Caltech-256, and Tubingen animals with attribute datasets, respectively. The results show that Dilated-MobileNets can obtain up to 2% higher classification accuracy than MobileNet.

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Deep neural networks are superior to dermatologists in melanoma image classification

European Journal of Cancer ◽

10.1016/j.ejca.2019.05.023 ◽

2019 ◽

Vol 119 ◽

pp. 11-17 ◽

Cited By ~ 48

Author(s):

Titus J. Brinker ◽

Achim Hekler ◽

Alexander H. Enk ◽

Carola Berking ◽

Sebastian Haferkamp ◽

...

Keyword(s):

Neural Networks ◽

Image Classification ◽

Deep Neural Networks

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Utilizing Information Bottleneck to Evaluate the Capability of Deep Neural Networks for Image Classification

Entropy ◽

10.3390/e21050456 ◽

2019 ◽

Vol 21 (5) ◽

pp. 456 ◽

Cited By ~ 3

Author(s):

Hao Cheng ◽

Dongze Lian ◽

Shenghua Gao ◽

Yanlin Geng

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Model Selection ◽

Image Classification ◽

Transfer Learning ◽

Deep Neural Networks ◽

Classification Problem ◽

Distribution Model ◽

Classification Problems ◽

Information Bottleneck

Inspired by the pioneering work of the information bottleneck (IB) principle for Deep Neural Networks’ (DNNs) analysis, we thoroughly study the relationship among the model accuracy, I ( X ; T ) and I ( T ; Y ) , where I ( X ; T ) and I ( T ; Y ) are the mutual information of DNN’s output T with input X and label Y. Then, we design an information plane-based framework to evaluate the capability of DNNs (including CNNs) for image classification. Instead of each hidden layer’s output, our framework focuses on the model output T. We successfully apply our framework to many application scenarios arising in deep learning and image classification problems, such as image classification with unbalanced data distribution, model selection, and transfer learning. The experimental results verify the effectiveness of the information plane-based framework: Our framework may facilitate a quick model selection and determine the number of samples needed for each class in the unbalanced classification problem. Furthermore, the framework explains the efficiency of transfer learning in the deep learning area.

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