MedViT: A Robust Vision Transformer for Generalized Medical Image
Classification
- URL: http://arxiv.org/abs/2302.09462v1
- Date: Sun, 19 Feb 2023 02:55:45 GMT
- Title: MedViT: A Robust Vision Transformer for Generalized Medical Image
Classification
- Authors: Omid Nejati Manzari, Hamid Ahmadabadi, Hossein Kashiani, Shahriar B.
Shokouhi, Ahmad Ayatollahi
- Abstract summary: We propose a robust yet efficient CNN-Transformer hybrid model which is equipped with the locality of CNNs and the global connectivity of vision Transformers.
Our proposed hybrid model demonstrates its high robustness and generalization ability compared to the state-of-the-art studies on a large-scale collection of standardized MedMNIST-2D datasets.
- Score: 4.471084427623774
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Convolutional Neural Networks (CNNs) have advanced existing medical systems
for automatic disease diagnosis. However, there are still concerns about the
reliability of deep medical diagnosis systems against the potential threats of
adversarial attacks since inaccurate diagnosis could lead to disastrous
consequences in the safety realm. In this study, we propose a highly robust yet
efficient CNN-Transformer hybrid model which is equipped with the locality of
CNNs as well as the global connectivity of vision Transformers. To mitigate the
high quadratic complexity of the self-attention mechanism while jointly
attending to information in various representation subspaces, we construct our
attention mechanism by means of an efficient convolution operation. Moreover,
to alleviate the fragility of our Transformer model against adversarial
attacks, we attempt to learn smoother decision boundaries. To this end, we
augment the shape information of an image in the high-level feature space by
permuting the feature mean and variance within mini-batches. With less
computational complexity, our proposed hybrid model demonstrates its high
robustness and generalization ability compared to the state-of-the-art studies
on a large-scale collection of standardized MedMNIST-2D datasets.
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