Explainable Machine Learning Framework for Image Classification Problems: Case Study on Glioma Cancer Prediction.
Explainable Machine Learning Framework for Image Classification Problems: Case Study on Glioma Cancer Prediction.
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DOI：
10.3390/jimaging6060037
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发表时间：
2020-05-28
影响因子：
3.2
通讯作者：
Pintelas P
中科院分区：
文献类型：
--
作者：
Pintelas E;Liaskos M;Livieris IE;Kotsiantis S;Pintelas P
图像分类是一个非常流行的机器学习领域，其中深度卷积神经网络主要出现在这样的应用中。这些网络在预测精度方面取得了显著的成绩，但它们被认为是黑箱模型，因为它们缺乏解释其内部工作机制和解释其预测的主要推理的能力。现实世界中有各种各样的任务，例如医学应用，其中可解释性和可解释性起着重要作用。在关键问题上做出决策，如利用黑箱模型进行癌症预测，以达到较高的预测精度，但没有提供任何形式的解释，其预测精度不能被认为是充分的和道德上可接受的。为了信任这些模型并支持这些关键的预测，推理和解释是必不可少的。然而，预测模型解释质量的定义和验证通常被认为是非常主观和不明确的。在这项工作中，提出了一个准确且可解释的机器学习框架，用于能够做出高质量解释的图像分类问题。为此，开发了一个特征提取和解释提取框架，并提出了三个基本的通用条件，用于验证任何模型在任何应用领域的预测解释质量。特征提取框架将为图像提取并创建透明且有意义的高级特征，而解释提取框架将根据这些提取的特征和预测模型的内部函数，针对所提出的条件，负责创建良好的解释。作为一个案例研究应用，脑肿瘤磁共振成像用于预测胶质瘤癌。我们的结果证明了所提出模型的效率，因为它成功地达到了足够的预测精度，并且可以用简单的人类术语解释和解释。
Image classification is a very popular machine learning domain in which deep convolutional neural networks have mainly emerged on such applications. These networks manage to achieve remarkable performance in terms of prediction accuracy but they are considered as black box models since they lack the ability to interpret their inner working mechanism and explain the main reasoning of their predictions. There is a variety of real world tasks, such as medical applications, in which interpretability and explainability play a significant role. Making decisions on critical issues such as cancer prediction utilizing black box models in order to achieve high prediction accuracy but without provision for any sort of explanation for its prediction, accuracy cannot be considered as sufficient and ethnically acceptable. Reasoning and explanation is essential in order to trust these models and support such critical predictions. Nevertheless, the definition and the validation of the quality of a prediction model’s explanation can be considered in general extremely subjective and unclear. In this work, an accurate and interpretable machine learning framework is proposed, for image classification problems able to make high quality explanations. For this task, it is developed a feature extraction and explanation extraction framework, proposing also three basic general conditions which validate the quality of any model’s prediction explanation for any application domain. The feature extraction framework will extract and create transparent and meaningful high level features for images, while the explanation extraction framework will be responsible for creating good explanations relying on these extracted features and the prediction model’s inner function with respect to the proposed conditions. As a case study application, brain tumor magnetic resonance images were utilized for predicting glioma cancer. Our results demonstrate the efficiency of the proposed model since it managed to achieve sufficient prediction accuracy being also interpretable and explainable in simple human terms.
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DOI：
10.1007/978-981-10-7272-7_3
发表时间：
2018-01-01
期刊：
MULTISCALE TRANSFORMS WITH APPLICATION TO IMAGE PROCESSING
影响因子：
--
作者：
Vyas, Aparna;Yu, Soohwan;Paik, Joonki
通讯作者：
Paik, Joonki
DOI：
10.1109/tsmc.1973.4309314
发表时间：
1973-01-01
期刊：
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS
影响因子：
--
作者：
HARALICK, RM;SHANMUGAM, K;DINSTEIN, I
通讯作者：
DINSTEIN, I
DOI：
10.1007/978-3-319-90403-0_9
发表时间：
2018-01-01
期刊：
HUMAN AND MACHINE LEARNING: VISIBLE, EXPLAINABLE, TRUSTWORTHY AND TRANSPARENT
影响因子：
--
作者：
Robnik-Sikonja, Marko;Bohanec, Marko
通讯作者：
Bohanec, Marko
影响因子：
18.6
作者：
Barredo Arrieta, Alejandro;Diaz-Rodriguez, Natalia;Herrera, Francisco
通讯作者：
Herrera, Francisco
DOI：
10.1109/tkde.2007.190734
发表时间：
2008-05-01
影响因子：
8.9
作者：
Robnik-Sikonja, Marko;Kononenko, Igor
通讯作者：
Kononenko, Igor