3D Convolutional Networks for Fully Automatic Fine-Grained Whole Heart Partition

2018 ◽  
pp. 181-189 ◽  
Author(s):  
Xin Yang ◽  
Cheng Bian ◽  
Lequan Yu ◽  
Dong Ni ◽  
Pheng-Ann Heng
Keyword(s):  
Fine Grained ◽  
Convolutional Networks ◽  
Fully Automatic ◽  
Whole Heart

Cascaded Volumetric Fully Convolutional Networks for Whole-Heart and Great Vessel 3D segmentation

2020 ◽  
Vol 108 ◽  
pp. 198-209
Author(s):  
Tao Han ◽  
Roberto F. Ivo ◽  
Douglas de A. Rodrigues ◽  
Solon A. Peixoto ◽  
Victor Hugo C. de Albuquerque ◽  
...  
Keyword(s):  
Great Vessel ◽  
3D Segmentation ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Whole Heart

scholarly journals Fully Automatic Speech-Based Analysis of the Semantic Verbal Fluency Task

2018 ◽  
Vol 45 (3-4) ◽  
pp. 198-209 ◽  
Author(s):  
Alexandra König ◽  
Nicklas Linz ◽  
Johannes Tröger ◽  
Maria Wolters ◽  
Jan Alexandersson ◽  
...  
Keyword(s):  
Task Performance ◽  
Verbal Fluency ◽  
Area Under Curve ◽  
Semantic Category ◽  
Verbal Fluency Task ◽  
Semantic Verbal Fluency ◽  
Fine Grained ◽  
Fully Automatic ◽  
Highly Correlated ◽  
Fluency Task

Background: Semantic verbal fluency (SVF) tests are routinely used in screening for mild cognitive impairment (MCI). In this task, participants name as many items as possible of a semantic category under a time constraint. Clinicians measure task performance manually by summing the number of correct words and errors. More fine-grained variables add valuable information to clinical assessment, but are time-consuming. Therefore, the aim of this study is to investigate whether automatic analysis of the SVF could provide these as accurate as manual and thus, support qualitative screening of neurocognitive impairment. Methods: SVF data were collected from 95 older people with MCI (n = 47), Alzheimer’s or related dementias (ADRD; n = 24), and healthy controls (HC; n = 24). All data were annotated manually and automatically with clusters and switches. The obtained metrics were validated using a classifier to distinguish HC, MCI, and ADRD. Results: Automatically extracted clusters and switches were highly correlated (r = 0.9) with manually established values, and performed as well on the classification task separating HC from persons with ADRD (area under curve [AUC] = 0.939) and MCI (AUC = 0.758). Conclusion: The results show that it is possible to automate fine-grained analyses of SVF data for the assessment of cognitive decline.

Temporal Convolutional Networks for Just-in-Time Design Smells Prediction using fine-grained Software Metrics

2021 ◽  
Author(s):  
Pasquale Ardimento ◽  
Lerina Aversano ◽  
Mario Luca Bernardi ◽  
Marta Cimitile ◽  
Martina Iammarino
Keyword(s):  
Software Metrics ◽  
Just In Time ◽  
Fine Grained ◽  
Convolutional Networks ◽  
Design Smells

scholarly journals Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks

2019 ◽  
Author(s):  
Hao Peng ◽  
Jianxin Li ◽  
Qiran Gong ◽  
Yangqiu Song ◽  
Yuanxin Ning ◽  
...  
Keyword(s):  
Semantic Relatedness ◽  
Real Data ◽  
Text Messages ◽  
Social Event ◽  
Convolutional Network ◽  
Fine Grained ◽  
Convolutional Networks ◽  
Social Events ◽  
Social Media Platforms ◽  
Data Collections

Events are happening in real-world and real-time, which can be planned and organized occasions involving multiple people and objects. Social media platforms publish a lot of text messages containing public events with comprehensive topics. However, mining social events is challenging due to the heterogeneous event elements in texts and explicit and implicit social network structures. In this paper, we design an event meta-schema to characterize the semantic relatedness of social events and build an event-based heterogeneous information network (HIN) integrating information from external knowledge base, and propose a novel Pairwise Popularity Graph Convolutional Network (PP-GCN) based fine-grained social event categorization model. We propose a Knowledgeable meta-paths Instances based social Event Similarity (KIES) between events and build a weighted adjacent matrix as input to the PP-GCN model. Comprehensive experiments on real data collections are conducted to compare various social event detection and clustering tasks. Experimental results demonstrate that our proposed framework outperforms other alternative social event categorization techniques.

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