MIXCAPS: A capsule network-based mixture of experts for lung nodule malignancy prediction

Highlights：

• Capsule networks (CapsNets) are developed aiming to overcome key drawbacks of the CNNs by identifying the spatial relations via their “Routing by Agreement” process.

• Mixture of Capsule networks (MIXCAPS), proposed in this work, for the task of lung nodule malignancy prediction, has the potential to improve the classification accuracy by integrating/coupling several CapsNet experts.

• Each CapsNet within the MIXCAPS architecture focuses on a specific subset of the nodules, therefore, improving the overall classification performance of the model.

• Output of the gating model is investigated for potential correlations with nodule hand-crafted features to improve the potential interpretability of the proposed MIXCAPS.

• Generalizability of the proposed MIXCAPS is illustrated via extension and evaluation based on a separate dataset associated with a different prediction task other than the one initially used to design the framework.

摘要

•Capsule networks (CapsNets) are developed aiming to overcome key drawbacks of the CNNs by identifying the spatial relations via their “Routing by Agreement” process.•Mixture of Capsule networks (MIXCAPS), proposed in this work, for the task of lung nodule malignancy prediction, has the potential to improve the classification accuracy by integrating/coupling several CapsNet experts.•Each CapsNet within the MIXCAPS architecture focuses on a specific subset of the nodules, therefore, improving the overall classification performance of the model.•Output of the gating model is investigated for potential correlations with nodule hand-crafted features to improve the potential interpretability of the proposed MIXCAPS.•Generalizability of the proposed MIXCAPS is illustrated via extension and evaluation based on a separate dataset associated with a different prediction task other than the one initially used to design the framework.