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arXiv · 1902.10053

Attention-driven Tree-structured Convolutional LSTM for High Dimensional Data Understanding

Bin Kong·Xin Wang·Junjie Bai·Yi Lu·Feng Gao·Kunlin Cao·Qi Song·Shaoting Zhang·Siwei Lyu·Youbing Yin
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Abstract

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.

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Bin Kong, Xin Wang, Junjie Bai, Yi Lu, Feng Gao, Kunlin Cao, Qi Song, Shaoting Zhang, Siwei Lyu, Youbing Yin. 2019-01-29. Attention-driven Tree-structured Convolutional LSTM for High Dimensional Data Understanding. https://arxiv.org/abs/1902.10053

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