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Progress in Chemistry 2022, Vol. 34 Issue (12): 2561-2572 DOI: 10.7536/PC220512   Next Articles

• CONTENTS •

Explainable Deep Learning in Spectral and Medical Image Analysis

Xuyang Liu1, Chaoshu Duan1, Wensheng Cai1,2, Xueguang Shao1,2()   

  1. 1 Research Center for Analytical Sciences, College of Chemistry, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, Nankai University,Tianjin 300071, China
    2 Haihe Laboratory of Sustainable Chemical Transformations,Tianjin 300192, China
  • Received: Revised: Online: Published:
  • Contact: Xueguang Shao
  • Supported by:
    National Natural Science Foundation of China(22174075); Natural Science Foundation of Tianjin, China(20JCYBJC01480); Haihe Laboratory of Sustainable Chemical Transformations(ZYTS202105)
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Deep learning is a modeling method based on neural network, which is constructed of multiple different functional perception layers and optimized by learning the inherent regularity of a large amount of data to achieve end-to-end modeling. The growth of data and the improvement of computing power promoted the applications of deep learning in spectral and medical image analysis. The lack of interpretability of the constructed models, however, constitutes an obstacle to their further development and applications. To overcome this obstacle of deep learning, various interpretability methods are proposed. According to different principles of explanation, interpretability methods are divided into three categories: visualization methods, model distillation, and interpretable models. Visualization methods and model distillation belong to external algorithms, which interpret a model without changing its structure, while interpretable models aim to make the model structure interpretable. In this review, the principles of deep learning and three interpretability methods are introduced from the perspective of algorithms. Moreover, the applications of the interpretability methods in spectral and medical image analysis in the past three years are summarized. In most studies, external algorithms were developed to make the models explainable, and these methods were found to be able to provide reasonable explanation for the abilities of the deep learning models. However, few studies attempt to construct interpretable algorithms within networks. Furthermore, most studies try to train the model through collecting large amounts of labeled data, which leads to huge costs in both labor and expenses. Therefore, training strategies with small data sets, approaches to enhance the interpretability of models, and the construction of interpretable deep learning architectures are still required in future work.

Contents

1 Introduction

2 Principle and algorithm

3 Interpretability method

3.1 Visualization method

3.2 Model distillation

3.3 Interpretable model

4 Spectral analysis

5 Medical image analysis

5.1 Segmentation

5.2 Disease diagnosis

6 Summary and outlook

Key words: deep learning, interpretability method, neural network, medical image analysis, spectral analysis
Fig. 1 Structure and calculation process of CNN
Fig. 2 Structure and calculation process of RNN
Fig. 3 Example of calculation process of grad-CAM algorithm
Fig. 4 Example of calculation process of integrated gradient algorithm
Fig. 5 Visualization of feature importance distribution by occlusion sensitivity algorithm
Fig. 6 Example of calculation process of LIME algorithm
Fig. 7 Model interpretation mechanism of SENNs
Fig. 8 Neural network classification strategy combined with PCA[49]. Reprinted with permission from [49]. Copyright 2022 American Chemical Society
Fig. 9 NIR-IIb image predicted by CycleGAN[55]
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