Improving Robustness of Medical Image Diagnosis with Denoising Convolutional Neural Networks

Xue, Fei-Fei; Jin, Peng; Wang, Ruixuan; Zhang, Qiong; Zheng, Wei‐Shi

doi:10.1007/978-3-030-32226-7_94

Cited by 17 publications

(7 citation statements)

References 10 publications

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“…Defence Method. In [44], the authors suggest embedding a denoising autoencoder neural network into the victim's DNN to mitigate noise such as adversarial perturbations. A diagram of the architecture is provided in Figure 3.…”

Section: Denoiser Methodsmentioning

confidence: 99%

The Security of Deep Learning Defences for Medical Imaging

Levy¹,

Amit²,

Elovici³

et al. 2022

Preprint

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Deep learning has shown great promise in the domain of medical image analysis. Medical professionals and healthcare providers have been adopting the technology to speed up and enhance their work. These systems use deep neural networks (DNN) which are vulnerable to adversarial samples; images with imperceivable changes that can alter the model's prediction. Researchers have proposed defences which either make a DNN more robust or detect the adversarial samples before they do harm. However, none of these works consider an informed attacker which can adapt to the defence mechanism. We show that an informed attacker can evade five of the current state of the art defences while successfully fooling the victim's deep learning model, rendering these defences useless. We then suggest better alternatives for securing healthcare DNNs from such attacks: (1) harden the system's security and (2) use digital signatures.

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Section: Denoiser Methodsmentioning

confidence: 99%

The Security of Deep Learning Defences for Medical Imaging

Levy¹,

Amit²,

Elovici³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Then, the original function becomes

. The residual learning approach achieves a good performance for denoising tasks by effectively resolving the vanishing gradient and the degradation problem [ 49 ].…”

Section: Methodsmentioning

confidence: 99%

Cascaded Convolutional Neural Network Architecture for Speech Emotion Recognition in Noisy Conditions

Nam

Lee

2021

Sensors

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Convolutional neural networks (CNNs) are a state-of-the-art technique for speech emotion recognition. However, CNNs have mostly been applied to noise-free emotional speech data, and limited evidence is available for their applicability in emotional speech denoising. In this study, a cascaded denoising CNN (DnCNN)–CNN architecture is proposed to classify emotions from Korean and German speech in noisy conditions. The proposed architecture consists of two stages. In the first stage, the DnCNN exploits the concept of residual learning to perform denoising; in the second stage, the CNN performs the classification. The classification results for real datasets show that the DnCNN–CNN outperforms the baseline CNN in overall accuracy for both languages. For Korean speech, the DnCNN–CNN achieves an accuracy of 95.8%, whereas the accuracy of the CNN is marginally lower (93.6%). For German speech, the DnCNN–CNN has an overall accuracy of 59.3–76.6%, whereas the CNN has an overall accuracy of 39.4–58.1%. These results demonstrate the feasibility of applying the DnCNN with residual learning to speech denoising and the effectiveness of the CNN-based approach in speech emotion recognition. Our findings provide new insights into speech emotion recognition in adverse conditions and have implications for language-universal speech emotion recognition.

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“…In the end-to-end deep learning approach, convolutional neural networks (CNN) is one of the most commonly used method and has a wide range of applications in computer vision-related tasks [86], [87]. Recently, CNN has promising applications in user authentication based on PPG signals [76].…”

Section: Authentication Modelmentioning

confidence: 99%

SoK: An Overview of PPG's Application in Authentication

Li¹,

Chen²,

Pan³

et al. 2022

Preprint

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Biometric authentication prospered during the 2010s. Vulnerability to spoofing attacks remains an inherent problem with traditional biometrics. Recently, unobservable physiological signals (e.g., Electroencephalography, Photoplethysmography, Electrocardiography) as biometrics have been considered a potential solution to this problem. In particular, Photoplethysmography (PPG) measures the change of blood flow of the human body by an optical method. Clinically, researchers commonly use PPG signals to obtain patients' blood oxygen saturation, heart rate, and other information to assist in diagnosing heart-related diseases. Since PPG signals are easy to obtain and contain a wealth of individual cardiac information, researchers have begun to explore its potential applications in information security. The unique advantages (simple acquisition, difficult to steal, and live detection) of the PPG signal allow it to improve the security and usability of the authentication in various aspects. However, the research on PPG-based authentication is still in its infancy. The lack of systematization hinders new research in this field. We conduct a comprehensive study of PPGbased authentication and discuss these applications' limitations before pointing out future research directions.

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Improving Robustness of Medical Image Diagnosis with Denoising Convolutional Neural Networks

Cited by 17 publications

References 10 publications

The Security of Deep Learning Defences for Medical Imaging

The Security of Deep Learning Defences for Medical Imaging

Cascaded Convolutional Neural Network Architecture for Speech Emotion Recognition in Noisy Conditions

SoK: An Overview of PPG's Application in Authentication

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