A novel adversarial learning strategy for medical image classification

• URL: http://arxiv.org/abs/2206.11501v2
• Date: Sun, 26 Jun 2022 22:28:21 GMT
• Title: A novel adversarial learning strategy for medical image classification
• Authors: Zong Fan, Xiaohui Zhang, Jacob A. Gasienica, Jennifer Potts, Su Ruan, Wade Thorstad, Hiram Gay, Xiaowei Wang, Hua Li
• Abstract summary: auxiliary convolutional neural networks (AuxCNNs) have been employed on top of traditional classification networks to facilitate the training of intermediate layers. In this study, we proposed an adversarial learning-based AuxCNN to support the training of deep neural networks for medical image classification.
• Score: 9.253330143870427
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning (DL) techniques have been extensively utilized for medical image classification. Most DL-based classification networks are generally structured hierarchically and optimized through the minimization of a single loss function measured at the end of the networks. However, such a single loss design could potentially lead to optimization of one specific value of interest but fail to leverage informative features from intermediate layers that might benefit classification performance and reduce the risk of overfitting. Recently, auxiliary convolutional neural networks (AuxCNNs) have been employed on top of traditional classification networks to facilitate the training of intermediate layers to improve classification performance and robustness. In this study, we proposed an adversarial learning-based AuxCNN to support the training of deep neural networks for medical image classification. Two main innovations were adopted in our AuxCNN classification framework. First, the proposed AuxCNN architecture includes an image generator and an image discriminator for extracting more informative image features for medical image classification, motivated by the concept of generative adversarial network (GAN) and its impressive ability in approximating target data distribution. Second, a hybrid loss function is designed to guide the model training by incorporating different objectives of the classification network and AuxCNN to reduce overfitting. Comprehensive experimental studies demonstrated the superior classification performance of the proposed model. The effect of the network-related factors on classification performance was investigated.

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