Multi-scale sparse denoising model based on non-separable wavelet

The prognosis of patients with lung adenocarcinoma (LUAD), especially early-stage LUAD, is dependent on clinicopathological features. However, its predictive utility is limited. In this study, we developed and trained a DeepRePath model based on a deep convolutional neural network (CNN) using multi-scale pathology images to predict the prognosis of patients with early-stage LUAD. DeepRePath was pre-trained with 1067 hematoxylin and eosin-stained whole-slide images of LUAD from the Cancer Genome Atlas. DeepRePath was further trained and validated using two separate CNNs and multi-scale pathology images of 393 resected lung cancer specimens from patients with stage I and II LUAD. Of the 393 patients, 95 patients developed recurrence after surgical resection. The DeepRePath model showed average area under the curve (AUC) scores of 0.77 and 0.76 in cohort I and cohort II (external validation set), respectively. Owing to low performance, DeepRePath cannot be used as an automated tool in a clinical setting. When gradient-weighted class activation mapping was used, DeepRePath indicated the association between atypical nuclei, discohesive tumor cells, and tumor necrosis in pathology images showing recurrence. Despite the limitations associated with a relatively small number of patients, the DeepRePath model based on CNNs with transfer learning could predict recurrence after the curative resection of early-stage LUAD using multi-scale pathology images.

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Image Segmentation with Multi-Scale GVF Snake Model Based on B-Spline Wavelet

Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007) ◽

10.1109/snpd.2007.498 ◽

2007 ◽

Author(s):

Zhang Jun ◽

Liu Jun

Keyword(s):

Image Segmentation ◽

Snake Model ◽

Spline Wavelet ◽

B Spline ◽

Multi Scale ◽

Model Based

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Building of robust multi-scale representations of LiDAR-based digital terrain model based on scale-space theory

Optics and Lasers in Engineering ◽

10.1016/j.optlaseng.2009.11.003 ◽

2010 ◽

Vol 48 (3) ◽

pp. 316-319 ◽

Cited By ~ 2

Author(s):

Tarig A. Ali

Keyword(s):

Digital Terrain Model ◽

Scale Space ◽

Space Theory ◽

Terrain Model ◽

Multi Scale ◽

Model Based ◽

Digital Terrain

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An Online Model-based Battery Parameter and State Estimation Method Using Multi-scale Dual Adaptive Particle Filters

Energy Procedia ◽

10.1016/j.egypro.2017.03.976 ◽

2017 ◽

Vol 105 ◽

pp. 4549-4554 ◽

Cited By ~ 6

Author(s):

Min Ye ◽

Hui Guo ◽

Rui Xiong ◽

Hao Mu

Keyword(s):

State Estimation ◽

Particle Filters ◽

Estimation Method ◽

Multi Scale ◽

Model Based

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A new multi-scale model based on CFD and macroscopic calculation for corrugated structured packing column

AIChE Journal ◽

10.1002/aic.14082 ◽

2013 ◽

Vol 59 (8) ◽

pp. 3119-3130 ◽

Cited By ~ 29

Author(s):

B. Sun ◽

L. He ◽

B.T. Liu ◽

F. Gu ◽

C.J. Liu

Keyword(s):

Scale Model ◽

Structured Packing ◽

Multi Scale ◽

Model Based ◽

Packing Column

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Optimal scale combination selection for inconsistent multi-scale decision tables

10.21203/rs.3.rs-635004/v1 ◽

2021 ◽

Author(s):

Yingjie Zhu ◽

Bin Yang

Keyword(s):

Lattice Model ◽

Selection Model ◽

Decision Table ◽

Scale Selection ◽

Multi Scale ◽

Optimal Scale ◽

Model Based ◽

Positive Region ◽

Decision Tables ◽

Combination Selection

Abstract Hierarchical structured data are very common for data mining and other tasks in real-life world. How to select the optimal scale combination from a multi-scale decision table is critical for subsequent tasks. At present, the models for calculating the optimal scale combination mainly include lattice model, complement model and stepwise optimal scale selection model, which are mainly based on consistent multi-scale decision tables. The optimal scale selection model for inconsistent multi-scale decision tables has not been given. Based on this, firstly, this paper introduces the concept of complement and lattice model proposed by Li and Hu. Secondly, based on the concept of positive region consistency of inconsistent multi-scale decision tables, the paper proposes complement model and lattice model based on positive region consistent and gives the algorithm. Finally, some numerical experiments are employed to verify that the model has the same properties in processing inconsistent multi-scale decision tables as the complement model and lattice model in processing consistent multi-scale decision tables. And for the consistent multi-scale decision table, the same results can be obtained by using the model based on positive region consistent. However, the lattice model based on positive region consistent is more time-consuming and costly. The model proposed in this paper provides a new theoretical method for the optimal scale combination selection of the inconsistent multi-scale decision table.

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A real-time stable volumetric Mass-Spring Model based on a multi-scale mesh representation

2016 23rd Iranian Conference on Biomedical Engineering and 2016 1st International Iranian Conference on Biomedical Engineering (ICBME) ◽

10.1109/icbme.2016.7890950 ◽

2016 ◽

Author(s):

Sepide Farhang ◽

Amir Hossein Foruzan ◽

Yen-Wei Chen

Keyword(s):

Real Time ◽

Spring Model ◽

Multi Scale ◽

Model Based ◽

Mass Spring Model ◽

Mass Spring

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A New Multi-Scale Convolutional Model Based on Multiple Attention for Image Classification

Applied Sciences ◽

10.3390/app10010101 ◽

2019 ◽

Vol 10 (1) ◽

pp. 101 ◽

Cited By ~ 3

Author(s):

Yadong Yang ◽

Chengji Xu ◽

Feng Dong ◽

Xiaofeng Wang

Keyword(s):

Image Classification ◽

Attention Mechanism ◽

Natural Scenes ◽

Significant Information ◽

Convolutional Networks ◽

Multi Scale ◽

Model Based ◽

Convolution Model ◽

Grouping Method ◽

Feature Expression

Computer vision systems are insensitive to the scale of objects in natural scenes, so it is important to study the multi-scale representation of features. Res2Net implements hierarchical multi-scale convolution in residual blocks, but its random grouping method affects the robustness and intuitive interpretability of the network. We propose a new multi-scale convolution model based on multiple attention. It introduces the attention mechanism into the structure of a Res2-block to better guide feature expression. First, we adopt channel attention to score channels and sort them in descending order of the feature’s importance (Channels-Sort). The sorted residual blocks are grouped and intra-block hierarchically convolved to form a single attention and multi-scale block (AMS-block). Then, we implement channel attention on the residual small blocks to constitute a dual attention and multi-scale block (DAMS-block). Introducing spatial attention before sorting the channels to form multi-attention multi-scale blocks(MAMS-block). A MAMS-convolutional neural network (CNN) is a series of multiple MAMS-blocks. It enables significant information to be expressed at more levels, and can also be easily grafted into different convolutional structures. Limited by hardware conditions, we only prove the validity of the proposed ideas through convolutional networks of the same magnitude. The experimental results show that the convolution model with an attention mechanism and multi-scale features is superior in image classification.

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