# TraceCaps: A Capsule-based Neural Network for Semantic Segmentation Source: [TraceCaps: A Capsule-based Neural Network for Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:43.721Z Authors: Tao Sun, Zhewei Wang, C. D. Smith, Jundong Liu Published: 2019-01-09T20:23:13.000Z ## Abstract In this paper, we propose a capsule-based neural network model to solve the semantic segmentation problem. By taking advantage of the extractable part-whole dependencies available in capsule layers, we derive the probabilities of the class labels for individual capsules through a recursive, layer-by-layer procedure. We model this procedure as a traceback pipeline and take it as a central piece to build an end-to-end segmentation network. Under the proposed framework, image-level class labels and object boundaries are jointly sought in an explicit manner, which poses a significant advantage over the state-of-the-art fully convolutional network (FCN) solutions. With the capability to extracted part-whole information, our traceback pipeline can potentially be utilized as the building blocks to design interpretable neural networks. Experiments conducted on modified MNIST and neuroimages demonstrate that our model considerably enhance the segmentation performance compared to the leading FCN variants. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()