Attention-driven Tree-structured Convolutional LSTM for High Dimensional Data Understanding
This addresses the problem of analyzing tree-structured image data in biomedical domains, representing an incremental advancement by adapting ConvLSTM to hierarchical structures.
The paper tackled the problem of modeling hierarchical data structures like coronary arteries in biomedical images, which sequential models like ConvLSTM cannot handle, by proposing a tree-structured ConvLSTM model; it achieved effective results validated on four large-scale coronary artery datasets.
Modeling the sequential information of image sequences has been a vital step of various vision tasks and convolutional long short-term memory (ConvLSTM) has demonstrated its superb performance in such spatiotemporal problems. Nevertheless, the hierarchical data structures in a significant amount of tasks (e.g., human body parts and vessel/airway tree in biomedical images) cannot be properly modeled by sequential models. Thus, ConvLSTM is not suitable for tree-structured image data analysis. In order to address these limitations, we present tree-structured ConvLSTM models for tree-structured image analysis tasks which can be trained end-to-end. To demonstrate the effectiveness of the proposed tree-structured ConvLSTM model, we present a tree-structured segmentation framework which consists of a tree-structured ConvLSTM and an attention fully convolutional network (FCN) model. The proposed framework is extensively validated on four large-scale coronary artery datasets. The results demonstrate the effectiveness and efficiency of the proposed method.