Principal wave analysis for high-dimensional structured data with applications to epigenomics and neuroimaging studies

2022 ◽  
Vol 15 (2) ◽  
pp. 225-236
Author(s):  
Yuping Zhang
Keyword(s):  
Structured Data ◽  
High Dimensional ◽  
Wave Analysis

scholarly journals Richer fusion network for breast cancer classification based on multimodal data

2021 ◽  
Vol 21 (S1) ◽  
Author(s):  
Rui Yan ◽  
Fa Zhang ◽  
Xiaosong Rao ◽  
Zhilong Lv ◽  
Jintao Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Clinical Practice ◽  
Structured Data ◽  
Cancer Classification ◽  
High Dimensional ◽  
Denoising Autoencoder ◽  
Multimodal Data ◽  
Breast Cancer Classification ◽  
Low Dimensional ◽  
Pathological Image

Abstract Background Deep learning algorithms significantly improve the accuracy of pathological image classification, but the accuracy of breast cancer classification using only single-mode pathological images still cannot meet the needs of clinical practice. Inspired by the real scenario of pathologists reading pathological images for diagnosis, we integrate pathological images and structured data extracted from clinical electronic medical record (EMR) to further improve the accuracy of breast cancer classification. Methods In this paper, we propose a new richer fusion network for the classification of benign and malignant breast cancer based on multimodal data. To make pathological image can be integrated more sufficient with structured EMR data, we proposed a method to extract richer multilevel feature representation of the pathological image from multiple convolutional layers. Meanwhile, to minimize the information loss for each modality before data fusion, we use the denoising autoencoder as a way to increase the low-dimensional structured EMR data to high-dimensional, instead of reducing the high-dimensional image data to low-dimensional before data fusion. In addition, denoising autoencoder naturally generalizes our method to make the accurate prediction with partially missing structured EMR data. Results The experimental results show that the proposed method is superior to the most advanced method in terms of the average classification accuracy (92.9%). In addition, we have released a dataset containing structured data from 185 patients that were extracted from EMR and 3764 paired pathological images of breast cancer, which can be publicly downloaded from http://ear.ict.ac.cn/?page_id=1663. Conclusions We utilized a new richer fusion network to integrate highly heterogeneous data to leverage the structured EMR data to improve the accuracy of pathological image classification. Therefore, the application of automatic breast cancer classification algorithms in clinical practice becomes possible. Due to the generality of the proposed fusion method, it can be straightforwardly extended to the fusion of other structured data and unstructured data.

High-Dimensional Probability

2018 ◽  
Author(s):  
Roman Vershynin
Keyword(s):  
High Dimensional

The Semantic Credentials of High-Dimensional Memory Models

2014 ◽  
Author(s):  
Curt Burgess ◽  
Sarah Maples
Keyword(s):  
Memory Models ◽  
High Dimensional

Wave Analysis of the Charge Density Wave Dynamics in the Molecular Conductor (Perylene)2Pt(mnt)2 (mnt=maleonitriledithiolate)

1995 ◽  
Vol 5 (5) ◽  
pp. 539-545 ◽  
Author(s):  
J. Dumas ◽  
N. Thirion ◽  
M. Almeida ◽  
E. B. Lopes ◽  
M. J. Matos ◽  
...  
Keyword(s):  
Charge Density ◽  
Charge Density Wave ◽  
Density Wave ◽  
Wave Analysis ◽  
Wave Dynamics ◽  
Molecular Conductor
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