Matwo-CapsNet: A Multi-label Semantic Segmentation Capsules Network
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Despite some design limitations, CNNs have been largely adopted by the computer vision community due to their efficacy and versatility. Introduced by Sabour et al. to circumvent some limitations of CNNs, capsules replace scalars with vectors to encode appearance feature representation, allowing better preservation of spatial relationships between whole objects and its parts. They also introduced the dynamic routing mechanism, which allows to weight the contributions of parts to a whole object differently at each inference step. Recently, Hinton et al. have proposed to solely encode pose information to model such part-whole relationships. Additionally, they used a matrix instead of a vector encoding in the capsules framework. In this work, we introduce several improvements to the capsules framework, allowing it to be applied for multi-label semantic segmentation. More specifically, we combine pose and appearance information encoded as matrices into a new type of capsule, i.e. Matwo-Caps. Additionally, we propose a novel routing mechanism, i.e. Dual Routing, which effectively combines these two kinds of information. We evaluate our resulting Matwo-CapsNet on the JSRT chest X-ray dataset by comparing it to SegCaps, a capsule based network for binary segmentation, as well as to other CNN based state-of-the-art segmentation methods, where we show that our Matwo-CapsNet achieves competitive results, while requiring only a fraction of the parameters of other previously proposed methods.
KeywordsCapsules network Convolutional neural network Chest X-ray Multi-label Semantic segmentation
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