Attention-driven Tree-structured Convolutional LSTM for High Dimensional Data Understanding
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Vocal Bursts Intensity PredictionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
To Attend or not to Attend: A Case Study on Syntactic Structures for Semantic Relatedness
With the recent success of Recurrent Neural Networks (RNNs) in Machine Translation (MT), attention mechanisms have become increasingly popular. The purpose of this paper is two-fold; firstly, we propose a novel attention…
Machine TranslationParaphrase IdentificationQuestion AnsweringSentence+1Biomedical Event Extraction based on Knowledge-driven Tree-LSTM
Event extraction for the biomedical domain is more challenging than that in the general news domain since it requires broader acquisition of domain-specific knowledge and deeper understanding of complex contexts. To bett…
Entity LinkingEvent ExtractionA constrained recursion algorithm for batch normalization of tree-sturctured LSTM
Tree-structured LSTM is promising way to consider long-distance interaction over hierarchies. However, there have been few research efforts on the hyperparameter tuning of the construction and traversal of tree-structure…
You Only Need Attention to Traverse Trees
In recent NLP research, a topic of interest is universal sentence encoding, sentence representations that can be used in any supervised task. At the word sequence level, fully attention-based models suffer from two probl…
SentenceTree-structured Attention with Hierarchical Accumulation
Incorporating hierarchical structures like constituency trees has been shown to be effective for various natural language processing (NLP) tasks. However, it is evident that state-of-the-art (SOTA) sequence-based models …
text-classificationText ClassificationTranslation