Universal Adversarial Framework to Improve Adversarial Robustness for Diabetic Retinopathy Detection

• URL: http://arxiv.org/abs/2312.08193v1
• Date: Wed, 13 Dec 2023 14:58:17 GMT
• Title: Universal Adversarial Framework to Improve Adversarial Robustness for Diabetic Retinopathy Detection
• Authors: Samrat Mukherjee, Dibyanayan Bandyopadhyay, Baban Gain, Asif Ekbal
• Abstract summary: Diabetic Retinopathy (DR) is a prevalent illness associated with Diabetes which, if left untreated, can result in irreversible blindness. Deep Learning based systems are gradually being introduced as automated support for clinical diagnosis. We use Universal Adversarial Perturbations (UAPs) to quantify the vulnerability of Medical Deep Neural Networks (DNNs) for detecting DR.
• Score: 33.08089616645845
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Diabetic Retinopathy (DR) is a prevalent illness associated with Diabetes which, if left untreated, can result in irreversible blindness. Deep Learning based systems are gradually being introduced as automated support for clinical diagnosis. Since healthcare has always been an extremely important domain demanding error-free performance, any adversaries could pose a big threat to the applicability of such systems. In this work, we use Universal Adversarial Perturbations (UAPs) to quantify the vulnerability of Medical Deep Neural Networks (DNNs) for detecting DR. To the best of our knowledge, this is the very first attempt that works on attacking complete fine-grained classification of DR images using various UAPs. Also, as a part of this work, we use UAPs to fine-tune the trained models to defend against adversarial samples. We experiment on several models and observe that the performance of such models towards unseen adversarial attacks gets boosted on average by $3.41$ Cohen-kappa value and maximum by $31.92$ Cohen-kappa value. The performance degradation on normal data upon ensembling the fine-tuned models was found to be statistically insignificant using t-test, highlighting the benefits of UAP-based adversarial fine-tuning.

Related papers

• Enhancing Diabetic Retinopathy Detection with CNN-Based Models: A Comparative Study of UNET and Stacked UNET Architectures [0.0]
  Diabetic Retinopathy DR is a severe complication of diabetes. Damaged or abnormal blood vessels can cause loss of vision. The need for massive screening of a large population of diabetic patients has generated an interest in a computer-aided fully automatic diagnosis of DR. Deep learning frameworks, particularly convolutional neural networks CNNs, have shown great interest and promise in detecting DR by analyzing retinal images.
  arXiv  Detail & Related papers  (2024-11-02T14:02:45Z)
• DiabetesNet: A Deep Learning Approach to Diabetes Diagnosis [6.095029229301643]
  Experimental results on three datasets show significant improvements in overall accuracy, sensitivity, and specificity compared to traditional methods. This underscores the potential of deep learning models for robust diabetes diagnosis.
  arXiv  Detail & Related papers  (2024-03-12T10:18:59Z)
• Generalizing to Unseen Domains in Diabetic Retinopathy Classification [8.59772105902647]
  We study the problem of generalizing a model to unseen distributions or domains in diabetic retinopathy classification. We propose a simple and effective domain generalization (DG) approach that achieves self-distillation in vision transformers. We report the performance of several state-of-the-art DG methods on open-source DR classification datasets.
  arXiv  Detail & Related papers  (2023-10-26T09:11:55Z)
• Automatic diagnosis of knee osteoarthritis severity using Swin transformer [55.01037422579516]
  Knee osteoarthritis (KOA) is a widespread condition that can cause chronic pain and stiffness in the knee joint. We propose an automated approach that employs the Swin Transformer to predict the severity of KOA.
  arXiv  Detail & Related papers  (2023-07-10T09:49:30Z)
• Blindness (Diabetic Retinopathy) Severity Scale Detection [0.0]
  Diabetic retinopathy (DR) is a severe complication of diabetes that can cause permanent blindness. Timely diagnosis and treatment of DR are critical to avoid total loss of vision. We propose a novel deep learning based method for automatic screening of retinal fundus images.
  arXiv  Detail & Related papers  (2021-10-04T11:31:15Z)
• Towards Adversarial Patch Analysis and Certified Defense against Crowd Counting [61.99564267735242]
  Crowd counting has drawn much attention due to its importance in safety-critical surveillance systems. Recent studies have demonstrated that deep neural network (DNN) methods are vulnerable to adversarial attacks. We propose a robust attack strategy called Adversarial Patch Attack with Momentum to evaluate the robustness of crowd counting models.
  arXiv  Detail & Related papers  (2021-04-22T05:10:55Z)
• Universal Adversarial Training with Class-Wise Perturbations [78.05383266222285]
  adversarial training is the most widely used method for defending against adversarial attacks. In this work, we find that a UAP does not attack all classes equally. We improve the SOTA UAT by proposing to utilize class-wise UAPs during adversarial training.
  arXiv  Detail & Related papers  (2021-04-07T09:05:49Z)
• Detecting COVID-19 from Breathing and Coughing Sounds using Deep Neural Networks [68.8204255655161]
  We adapt an ensemble of Convolutional Neural Networks to classify if a speaker is infected with COVID-19 or not. Ultimately, it achieves an Unweighted Average Recall (UAR) of 74.9%, or an Area Under ROC Curve (AUC) of 80.7% by ensembling neural networks.
  arXiv  Detail & Related papers  (2020-12-29T01:14:17Z)
• Adversarial Exposure Attack on Diabetic Retinopathy Imagery Grading [75.73437831338907]
  Diabetic Retinopathy (DR) is a leading cause of vision loss around the world. To help diagnose it, numerous cutting-edge works have built powerful deep neural networks (DNNs) to automatically grade DR via retinal fundus images (RFIs) RFIs are commonly affected by camera exposure issues that may lead to incorrect grades. In this paper, we study this problem from the viewpoint of adversarial attacks.
  arXiv  Detail & Related papers  (2020-09-19T13:47:33Z)
• Blended Multi-Modal Deep ConvNet Features for Diabetic Retinopathy Severity Prediction [0.0]
  Diabetic Retinopathy (DR) is one of the major causes of visual impairment and blindness across the world. To derive optimal representation of retinal images, features extracted from multiple pre-trained ConvNet models are blended. We achieve an accuracy of 97.41%, and a kappa statistic of 94.82 for DR identification and an accuracy of 81.7% and a kappa statistic of 71.1% for severity level prediction.
  arXiv  Detail & Related papers  (2020-05-30T06:46:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.