Out of Distribution Detection and Adversarial Attacks on Deep Neural Networks for Robust Medical Image Analysis

• URL: http://arxiv.org/abs/2107.04882v1
• Date: Sat, 10 Jul 2021 18:00:40 GMT
• Title: Out of Distribution Detection and Adversarial Attacks on Deep Neural Networks for Robust Medical Image Analysis
• Authors: Anisie Uwimana1, Ransalu Senanayake
• Abstract summary: We experimentally evaluate the robustness of a Mahalanobis distance-based confidence score, a simple yet effective method for detecting abnormal input samples. Results indicated that the Mahalanobis confidence score detector exhibits improved performance and robustness of deep learning models.
• Score: 8.985261743452988
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning models have become a popular choice for medical image analysis. However, the poor generalization performance of deep learning models limits them from being deployed in the real world as robustness is critical for medical applications. For instance, the state-of-the-art Convolutional Neural Networks (CNNs) fail to detect adversarial samples or samples drawn statistically far away from the training distribution. In this work, we experimentally evaluate the robustness of a Mahalanobis distance-based confidence score, a simple yet effective method for detecting abnormal input samples, in classifying malaria parasitized cells and uninfected cells. Results indicated that the Mahalanobis confidence score detector exhibits improved performance and robustness of deep learning models, and achieves stateof-the-art performance on both out-of-distribution (OOD) and adversarial samples.

Related papers

• Dimensionality Reduction and Nearest Neighbors for Improving Out-of-Distribution Detection in Medical Image Segmentation [1.2873975765521795]
  This work applied the Mahalanobis distance (MD) post hoc to the bottleneck features of four Swin UNETR and nnU-net models that segmented the liver. Images the models failed on were detected with high performance and minimal computational load.
  arXiv  Detail & Related papers  (2024-08-05T18:24:48Z)
• Inadequacy of common stochastic neural networks for reliable clinical decision support [0.4262974002462632]
  Widespread adoption of AI for medical decision making is still hindered due to ethical and safety-related concerns. Common deep learning approaches, however, have the tendency towards overconfidence under data shift. This study investigates their actual reliability in clinical applications.
  arXiv  Detail & Related papers  (2024-01-24T18:49:30Z)
• Data Attribution for Diffusion Models: Timestep-induced Bias in Influence Estimation [53.27596811146316]
  Diffusion models operate over a sequence of timesteps instead of instantaneous input-output relationships in previous contexts. We present Diffusion-TracIn that incorporates this temporal dynamics and observe that samples' loss gradient norms are highly dependent on timestep. We introduce Diffusion-ReTrac as a re-normalized adaptation that enables the retrieval of training samples more targeted to the test sample of interest.
  arXiv  Detail & Related papers  (2024-01-17T07:58:18Z)
• Projection Regret: Reducing Background Bias for Novelty Detection via Diffusion Models [72.07462371883501]
  We propose emphProjection Regret (PR), an efficient novelty detection method that mitigates the bias of non-semantic information. PR computes the perceptual distance between the test image and its diffusion-based projection to detect abnormality. Extensive experiments demonstrate that PR outperforms the prior art of generative-model-based novelty detection methods by a significant margin.
  arXiv  Detail & Related papers  (2023-12-05T09:44:47Z)
• Energy-based Out-of-Distribution Detection for Graph Neural Networks [76.0242218180483]
  We propose a simple, powerful and efficient OOD detection model for GNN-based learning on graphs, which we call GNNSafe. GNNSafe achieves up to $17.0%$ AUROC improvement over state-of-the-arts and it could serve as simple yet strong baselines in such an under-developed area.
  arXiv  Detail & Related papers  (2023-02-06T16:38:43Z)
• Detecting when pre-trained nnU-Net models fail silently for Covid-19 lung lesion segmentation [0.34940201626430645]
  We propose a lightweight OOD detection method that exploits the Mahalanobis distance in the feature space. We validate our method with a patch-based nnU-Net architecture trained with a multi-institutional dataset.
  arXiv  Detail & Related papers  (2021-07-13T10:48:08Z)
• Residual Error: a New Performance Measure for Adversarial Robustness [85.0371352689919]
  A major challenge that limits the wide-spread adoption of deep learning has been their fragility to adversarial attacks. This study presents the concept of residual error, a new performance measure for assessing the adversarial robustness of a deep neural network. Experimental results using the case of image classification demonstrate the effectiveness and efficacy of the proposed residual error metric.
  arXiv  Detail & Related papers  (2021-06-18T16:34:23Z)
• Quantifying Predictive Uncertainty in Medical Image Analysis with Deep Kernel Learning [14.03923026690186]
  We propose an uncertainty-aware deep kernel learning model which permits the estimation of the uncertainty in the prediction. In most cases, the proposed model shows better performance compared to common architectures. Our model can also be used to detect challenging and controversial test samples.
  arXiv  Detail & Related papers  (2021-06-01T17:09:47Z)
• Adversarial Examples Detection with Bayesian Neural Network [57.185482121807716]
  We propose a new framework to detect adversarial examples motivated by the observations that random components can improve the smoothness of predictors. We propose a novel Bayesian adversarial example detector, short for BATer, to improve the performance of adversarial example detection.
  arXiv  Detail & Related papers  (2021-05-18T15:51:24Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• Closeness and Uncertainty Aware Adversarial Examples Detection in Adversarial Machine Learning [0.7734726150561088]
  We explore and assess the usage of 2 different groups of metrics in detecting adversarial samples. We introduce a new feature for adversarial detection, and we show that the performances of all these metrics heavily depend on the strength of the attack being used.
  arXiv  Detail & Related papers  (2020-12-11T14:44:59Z)

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.