scholarly journals Regional Registration of Whole Slide Image Stacks Containing Major Histological Artifacts

2020 ◽  
Author(s):  
Mahsa Paknezhad ◽  
Sheng Yang Michael Loh ◽  
Yukti Choudhury ◽  
Valerie Koh Cui Koh ◽  
Timothy Tay Kwang Yong ◽  
...  
Keyword(s):  
Patch Size ◽  
State Of The Art ◽  
The State ◽  
3D Analysis ◽  
Image Stack ◽  
Tissue Volume ◽  
Multi Scale ◽  
Registration Algorithm ◽  
Regional Registration ◽  
Whole Slide Images

Abstract Background: High resolution 2D whole slide imaging provides rich information about the tissue structure. This information can be a lot richer if these 2D images can be stacked into a 3D tissue volume. A 3D analysis, however, requires accurate reconstruction of the tissue volume from the 2D image stack. This task is not trivial due to the distortions such as tissue tearing, folding and missing at each slide. Performing registration for the whole tissue slices may be adversely affected by distorted tissue regions. Consequently, regional registration is found to be more effective. In this paper, we propose a new approach to an accurate and robust registration of regions of interest for whole slide images. We introduce the idea of multi-scale attention for registration. Results: Using mean similarity index as the metric, the proposed algorithm (mean +- std: 0.84 +- 0.11) followed by a fine registration algorithm (0.86 +- 0.08) outperformed the state-of-the-art linear whole tissue registration algorithm (0.74 +- 0.19) and the regional version of this algorithm (0.81 +- 0.15). The proposed algorithm also outperforms the state-of-the-art nonlinear registration algorithm (original: 0.82 +- 0.12, regional: 0.77 +- 0.22) for whole slide images and a recently proposed patch-based registration algorithm (patch size 256: 0.79 +- 0.16 , patch size 512: 0.77 +- 0.16) for medical images. Conclusion: Using multi-scale attention mechanism leads to a more robust and accurate solution to the problem of regional registration of whole slide images corrupted in some parts by major histological artifacts in the imaged tissue.

scholarly journals Regional Registration of Whole Slide Image Stacks Containing Major Histological Artifacts

2020 ◽  
Author(s):  
Mahsa Paknezhad ◽  
Sheng Yang Michael Loh ◽  
Yukti Choudhury ◽  
Valerie Koh Cui Koh ◽  
Timothy Tay Kwang Yong ◽  
...  
Keyword(s):  
Patch Size ◽  
State Of The Art ◽  
The State ◽  
3D Analysis ◽  
Image Stack ◽  
Tissue Volume ◽  
Multi Scale ◽  
Registration Algorithm ◽  
Regional Registration ◽  
Whole Slide Images

Abstract Background: High resolution 2D whole slide imaging provides rich information about the tissue structure. This information can be a lot richer if these 2D images can be stacked into a 3D tissue volume. A 3D analysis, however, requires accurate reconstruction of the tissue volume from the 2D image stack. This task is not trivial due to the distortions such as tissue tearing, folding and missing at each slide. Performing registration for the whole tissue slices may be adversely affected by distorted tissue regions. Consequently, regional registration is found to be more effective. In this paper, we propose a new approach to an accurate and robust registration of regions of interest for whole slide images. We introduce the idea of multi-scale attention for registration. Results: Using mean similarity index as the metric, the proposed algorithm (mean +- std: 0.84 +- 0.11) followed by a fine registration algorithm (0.86 +- 0.08) outperformed the state-of-the-art linear whole tissue registration algorithm (0.74 +- 0.19) and the regional version of this algorithm (0.81 +- 0.15). The proposed algorithm also outperforms the state-of-the-art nonlinear registration algorithm (original: 0.82 +- 0.12, regional: 0.77 +- 0.22) for whole slide images and a recently proposed patch-based registration algorithm (patch size 256: 0.79 +- 0.16 , patch size 512: 0.77 +- 0.16) for medical images. Conclusion: Using multi-scale attention mechanism leads to a more robust and accurate solution to the problem of regional registration of whole slide images corrupted in some parts by major histological artifacts in the imaged tissue.

scholarly journals Regional registration of whole slide image stacks containing major histological artifacts

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Mahsa Paknezhad ◽  
Sheng Yang Michael Loh ◽  
Yukti Choudhury ◽  
Valerie Koh Cui Koh ◽  
Timothy Tay Kwang Yong ◽  
...  
Keyword(s):  
Patch Size ◽  
State Of The Art ◽  
The State ◽  
3D Analysis ◽  
Image Stack ◽  
Tissue Volume ◽  
Multi Scale ◽  
Registration Algorithm ◽  
Regional Registration ◽  
Whole Slide Images

Abstract Background High resolution 2D whole slide imaging provides rich information about the tissue structure. This information can be a lot richer if these 2D images can be stacked into a 3D tissue volume. A 3D analysis, however, requires accurate reconstruction of the tissue volume from the 2D image stack. This task is not trivial due to the distortions such as tissue tearing, folding and missing at each slide. Performing registration for the whole tissue slices may be adversely affected by distorted tissue regions. Consequently, regional registration is found to be more effective. In this paper, we propose a new approach to an accurate and robust registration of regions of interest for whole slide images. We introduce the idea of multi-scale attention for registration. Results Using mean similarity index as the metric, the proposed algorithm (mean ± SD $$0.84 \pm 0.11$$ 0.84 ± 0.11 ) followed by a fine registration algorithm ($$0.86 \pm 0.08$$ 0.86 ± 0.08 ) outperformed the state-of-the-art linear whole tissue registration algorithm ($$0.74 \pm 0.19$$ 0.74 ± 0.19 ) and the regional version of this algorithm ($$0.81 \pm 0.15$$ 0.81 ± 0.15 ). The proposed algorithm also outperforms the state-of-the-art nonlinear registration algorithm (original: $$0.82 \pm 0.12$$ 0.82 ± 0.12 , regional: $$0.77 \pm 0.22$$ 0.77 ± 0.22 ) for whole slide images and a recently proposed patch-based registration algorithm (patch size 256: $$0.79 \pm 0.16$$ 0.79 ± 0.16 , patch size 512: $$0.77 \pm 0.16$$ 0.77 ± 0.16 ) for medical images. Conclusion Using multi-scale attention mechanism leads to a more robust and accurate solution to the problem of regional registration of whole slide images corrupted in some parts by major histological artifacts in the imaged tissue.

scholarly journals Regional Registration of Whole Slide Image Stacks Containing Highly Deformed Artefacts

2020 ◽  
Author(s):  
Mahsa Paknezhad ◽  
Sheng Yang Michael Loh ◽  
Yukti Choudhury ◽  
Valerie Koh Cui Koh ◽  
Timothy Tay Kwang Yong ◽  
...  
Keyword(s):  
Patch Size ◽  
State Of The Art ◽  
Similarity Index ◽  
The State ◽  
3D Analysis ◽  
Image Stack ◽  
Tissue Volume ◽  
Registration Algorithm ◽  
Regional Registration ◽  
Whole Slide Images

Abstract Background: High resolution 2D whole slide imaging provides rich information about the tissue structure. This information can be a lot richer if these 2D images can be stacked into a 3D tissue volume. A 3D analysis, however, requires accurate reconstruction of the tissue volume from the 2D image stack. This task is not trivial due to the distortions that each individual tissue slice experiences while cutting and mounting the tissue on the glass slide. Performing registration for the whole tissue slices may be adversely affected by the deformed tissue regions. Consequently, regional registration is found to be more effective. In this paper, we propose an accurate and robust regional registration algorithm for whole slide images which incrementally focuses registration on the area around the region of interest. Results: Using mean similarity index as the metric, the proposed algorithm (mean +- std: 0.84 +- 0.11) followed by a fine registration algorithm (0.86 +- 0.08) outperformed the state-of-the-art linear whole tissue registration algorithm (0.74 +- 0.19) and the regional version of this algorithm (0.81 +- 0.15). The proposed algorithm also outperforms the state-of-the-art nonlinear registration algorithm (original: 0.82 +- 0.12, regional: 0.77 +- 0.22) for whole slide images and a recently proposed patch-based registration algorithm (patch size 256: 0.79 +- 0.16 , patch size 512: 0.77 +- 0.16) for medical images. Conclusion: The proposed algorithm is a more robust and accurate solution to the problem of regional registration of whole slide images in existence of highly deformed regions in the imaged tissue.

scholarly journals Encoding Time Series as Multi-Scale Signed Recurrence Plots for Classification Using Fully Convolutional Networks

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3818
Author(s):  
Ye Zhang ◽  
Yi Hou ◽  
Shilin Zhou ◽  
Kewei Ouyang
Keyword(s):  
Time Series ◽  
State Of The Art ◽  
Recurrence Plot ◽  
The State ◽  
Recurrence Plots ◽  
Convolutional Networks ◽  
Multi Scale ◽  
Fully Convolutional Networks ◽  
Benchmark Datasets ◽  
Visualization Evaluation

Recent advances in time series classification (TSC) have exploited deep neural networks (DNN) to improve the performance. One promising approach encodes time series as recurrence plot (RP) images for the sake of leveraging the state-of-the-art DNN to achieve accuracy. Such an approach has been shown to achieve impressive results, raising the interest of the community in it. However, it remains unsolved how to handle not only the variability in the distinctive region scale and the length of sequences but also the tendency confusion problem. In this paper, we tackle the problem using Multi-scale Signed Recurrence Plots (MS-RP), an improvement of RP, and propose a novel method based on MS-RP images and Fully Convolutional Networks (FCN) for TSC. This method first introduces phase space dimension and time delay embedding of RP to produce multi-scale RP images; then, with the use of asymmetrical structure, constructed RP images can represent very long sequences (>700 points). Next, MS-RP images are obtained by multiplying designed sign masks in order to remove the tendency confusion. Finally, FCN is trained with MS-RP images to perform classification. Experimental results on 45 benchmark datasets demonstrate that our method improves the state-of-the-art in terms of classification accuracy and visualization evaluation.

scholarly journals Antimicrobial peptide identification using multi-scale convolutional network

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Xin Su ◽  
Jing Xu ◽  
Yanbin Yin ◽  
Xiongwen Quan ◽  
Han Zhang
Keyword(s):  
Deep Learning ◽  
Antimicrobial Peptide ◽  
State Of The Art ◽  
Peptide Identification ◽  
The State ◽  
Fusion Model ◽  
Learning Methods ◽  
Convolutional Network ◽  
Multi Scale ◽  
Peptide Database

Abstract Background Antibiotic resistance has become an increasingly serious problem in the past decades. As an alternative choice, antimicrobial peptides (AMPs) have attracted lots of attention. To identify new AMPs, machine learning methods have been commonly used. More recently, some deep learning methods have also been applied to this problem. Results In this paper, we designed a deep learning model to identify AMP sequences. We employed the embedding layer and the multi-scale convolutional network in our model. The multi-scale convolutional network, which contains multiple convolutional layers of varying filter lengths, could utilize all latent features captured by the multiple convolutional layers. To further improve the performance, we also incorporated additional information into the designed model and proposed a fusion model. Results showed that our model outperforms the state-of-the-art models on two AMP datasets and the Antimicrobial Peptide Database (APD)3 benchmark dataset. The fusion model also outperforms the state-of-the-art model on an anti-inflammatory peptides (AIPs) dataset at the accuracy. Conclusions Multi-scale convolutional network is a novel addition to existing deep neural network (DNN) models. The proposed DNN model and the modified fusion model outperform the state-of-the-art models for new AMP discovery. The source code and data are available at https://github.com/zhanglabNKU/APIN.

scholarly journals Boosting Texture-Based Classification by Describing Statistical Information of Gray-Levels Differences

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1048 ◽  
Author(s):  
Óscar García-Olalla ◽  
Laura Fernández-Robles ◽  
Enrique Alegre ◽  
Manuel Castejón-Limas ◽  
Eduardo Fidalgo
Keyword(s):  
Face Recognition ◽  
State Of The Art ◽  
Local Binary Patterns ◽  
Statistical Information ◽  
The State ◽  
Texture Descriptor ◽  
Texture Recognition ◽  
Material Recognition ◽  
Multi Scale ◽  
Hit Rate

This paper presents a new texture descriptor booster, Complete Local Oriented Statistical Information Booster (CLOSIB), based on statistical information of the image. Our proposal uses the statistical information of the texture provided by the image gray-levels differences to increase the discriminative capability of Local Binary Patterns (LBP)-based and other texture descriptors. We demonstrated that Half-CLOSIB and M-CLOSIB versions are more efficient and precise than the general one. H-CLOSIB may eliminate redundant statistical information and the multi-scale version, M-CLOSIB, is more robust. We evaluated our method using four datasets: KTH TIPS (2-a) for material recognition, UIUC and USPTex for general texture recognition and JAFFE for face recognition. The results show that when we combine CLOSIB with well-known LBP-based descriptors, the hit rate increases in all the cases, introducing in this way the idea that CLOSIB can be used to enhance the description of texture in a significant number of situations. Additionally, a comparison with recent algorithms demonstrates that a combination of LBP methods with CLOSIB variants obtains comparable results to those of the state-of-the-art.

scholarly journals Region-Enhancing Network for Semantic Segmentation of Remote-Sensing Imagery

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7316
Author(s):  
Bo Zhong ◽  
Jiang Du ◽  
Minghao Liu ◽  
Aixia Yang ◽  
Junjun Wu
Keyword(s):  
Remote Sensing ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
The State ◽  
Learning Ability ◽  
Remote Sensing Imagery ◽  
Multi Scale ◽  
Context Learning ◽  
Learning Procedure ◽  
Public Datasets

Semantic segmentation for high-resolution remote-sensing imagery (HRRSI) has become increasingly popular in machine vision in recent years. Most of the state-of-the-art methods for semantic segmentation of HRRSI usually emphasize the strong learning ability of deep convolutional neural network to model the contextual relationship in the image, which takes too much consideration on every pixel in images and subsequently causes the problem of overlearning. Annotation errors and easily confused features can also lead to the confusion problem while using the pixel-based methods. Therefore, we propose a new semantic segmentation network—the region-enhancing network (RE-Net)—to emphasize the regional information instead of pixels to solve the above problems. RE-Net introduces the regional information into the base network, to enhance the regional integrity of images and thus reduce misclassification. Specifically, the regional context learning procedure (RCLP) can learn the context relationship from the perspective of regions. The region correcting procedure (RCP) uses the pixel aggregation feature to recalibrate the pixel features in each region. In addition, another simple intra-network multi-scale attention module is introduced to select features at different scales by the size of the region. A large number of comparative experiments on four different public datasets demonstrate that the proposed RE-Net performs better than most of the state-of-the-art ones.

scholarly journals A Multi-Scale Approach for Graph Link Prediction

2020 ◽  
Vol 34 (04) ◽  
pp. 3308-3315 ◽  
Author(s):  
Lei Cai ◽  
Shuiwang Ji
Keyword(s):  
Neural Networks ◽  
Link Prediction ◽  
Prediction Models ◽  
State Of The Art ◽  
The State ◽  
Graph Structure ◽  
Scale Invariant ◽  
Multi Scale ◽  
Heuristic Learning ◽  
Graph Neural Networks

Deep models can be made scale-invariant when trained with multi-scale information. Images can be easily made multi-scale, given their grid-like structures. Extending this to generic graphs poses major challenges. For example, in link prediction tasks, inputs are represented as graphs consisting of nodes and edges. Currently, the state-of-the-art model for link prediction uses supervised heuristic learning, which learns graph structure features centered on two target nodes. It then learns graph neural networks to predict the existence of links based on graph structure features. Thus, the performance of link prediction models highly depends on graph structure features. In this work, we propose a novel node aggregation method that can transform the enclosing subgraph into different scales and preserve the relationship between two target nodes for link prediction. A theory for analyzing the information loss during the re-scaling procedure is also provided. Graphs in different scales can provide scale-invariant information, which enables graph neural networks to learn invariant features and improve link prediction performance. Our experimental results on 14 datasets from different areas demonstrate that our proposed method outperforms the state-of-the-art methods by employing multi-scale graphs without additional parameters.

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