Abstract
Spiking neural networks (SNNs) have the advantages of inherent power-efficiency, biological plausibility and good image recognition performance. They are good candidates for medical image classification especially when the labeled training data are limited. In medical image classification, one of the major challenges is the highly class imbalanced problem which causes deep learning networks to bias towards the majority class and poorly recognizes the minority class. Despite that there are some methods for addressing this problem, very few algorithm-level methods exist for SNNs. In this work, we propose an imbalanced reward-modulation spike-timing-dependent plasticity (R-STDP) learning rule for SNNs to solve the medical image class imbalanced problem. We introduce an imbalanced reward coefficient for the R-STDP learning rule to set the reward from the minority class to be higher than that of the majority class, and this reward coefficient can help to set the class-dependent rewards according to the data statistic of the training dataset. Experiment results on three benchmark datasets with imbalanced splits show that our method significantly improves the performance than that of the baseline SNNs and outperforms the compared state-of-the-art methods addressing medical image class imbalanced problem including data-level and algorithm-level methods. Moreover, our method achieves excellent classification performance on the imbalanced medical dataset ISIC-2018. The results show that the proposed method can well help SNNs in classifying imbalanced medical image datasets. Besides, our proposed method can obtain high sensitivity to disease class by adjusting the reward coefficient, which is very useful for identifying disease samples in medical diagnostic tasks.
Highlights
T HE future of medicine places much emphasis on early diagnosis and personalized medicine [1]
This is the first work that modifies the learning algorithm of SNNs to solve the problem of class imbalance
Our contribution consists of the following: 1) we study the impact of class imbalance on the classification performance of SNNs. 2) We propose an imbalanced reward-modulation spike-timing-dependent plasticity (R-STDP) learning rule by introducing the reward coefficient, and prove its effect on the learning of SNNs through derivation
Summary
T HE future of medicine places much emphasis on early diagnosis and personalized medicine [1]. There are some SNNs whose classification performances are comparable to traditional convolutional neural networks (CNNs) for small-scale image recognition tasks [33]–[35] Another advantage of SNNs is that they can learn and achieve better results than deep learning models when learning with fewer data [32]. Huang et al [59] applied data augmentation to eliminate the effect of the class imbalance, and using Inception-v4 networks they achieved better performance over the state-of-the-art methods on the public available ISIC skin lesion challenge datasets in 2018. All of these spikes will to be processed in the following convolutional layer
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