Abstract
The recent success of convolutional neural networks (CNNs) attracts much attention to applying a computer-aided diagnosis system for digital pathology. However, the basis of CNN’s decision is incomprehensible for humans due to its complexity, and this will reduce the reliability of its decision. We improve the interpretability of the decision made using the CNN by presenting them as co-occurrences of interpretable components which typically appeared in parts of images. To this end, we propose a prototype-based interpretation method and define prototypes as the components. The method comprises the following three approaches: (1) presenting typical parts of images as multiple components, (2) allowing humans to interpret the components visually, and (3) making decisions based on the co-occurrence relation of the multiple components. Concretely, we first encode image patches using the encoder of a variational auto-encoder (VAE) and construct clusters for the encoded image patches to obtain prototypes. We then decode prototypes into images using the VAE’s decoder to make the prototypes visually interpretable. Finally, we calculate the weighted combinations of the prototype occurrences for image-level classification. The weights enable us to ascertain the prototypes that contributed to decision-making. We verified both the interpretability and classification performance of our method through experiments using two types of datasets. The proposed method showed a significant advantage for interpretation by displaying the association between class discriminative components in an image and the prototypes.
The part of this work is based on results obtained from a project commissioned by the New Energy and Industrial Technology Development Organization (NEDO).
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Uehara, K., Murakawa, M., Nosato, H., Sakanashi, H. (2020). Prototype-Based Interpretation of Pathological Image Analysis by Convolutional Neural Networks. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W. (eds) Pattern Recognition. ACPR 2019. Lecture Notes in Computer Science(), vol 12047. Springer, Cham. https://doi.org/10.1007/978-3-030-41299-9_50
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