Purpose Understanding decisions of deep learning techniques is important. Especially in the medical ﬁeld, the reasons for a decision in a classiﬁcation task are as crucial as the pure classiﬁcation results. In this article, we propose a new approach to compute relevant parts of a medical image. Knowing the relevant parts makes it easier to understand decisions. Methods In our approach, a convolutional neural network is employed to learn structures of images of lung nodules. Then, an evolutionary algorithm is applied to compute a simpliﬁed version of an unknown image based on the learned structures by the convolutional neural network. In the simpliﬁed version, irrelevant parts are removed from the original image. Results In the results, we show simpliﬁed images which allow the observer to focus on the relevant parts. In these images, more than 50% of the pixels are simpliﬁed. The simpliﬁed pixels do not change the meaning of the images based on the learned structures by the convolutional neural network. An experimental analysis shows the potential of the approach. Besides the examples of simpliﬁed images, we analyze the run time development. Conclusions Simpliﬁed images make it easier to focus on relevant parts and to ﬁnd reasons for
International Journal of Computer Assisted Radiology and Surgery – Springer Journals
Published: May 29, 2018
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