1
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Longo LHDC, Roberto GF, Tosta TAA, de Faria PR, Loyola AM, Cardoso SV, Silva AB, do Nascimento MZ, Neves LA. Classification of Multiple H&E Images via an Ensemble Computational Scheme. ENTROPY (BASEL, SWITZERLAND) 2023; 26:34. [PMID: 38248160 PMCID: PMC10814107 DOI: 10.3390/e26010034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 12/23/2023] [Accepted: 12/25/2023] [Indexed: 01/23/2024]
Abstract
In this work, a computational scheme is proposed to identify the main combinations of handcrafted descriptors and deep-learned features capable of classifying histological images stained with hematoxylin and eosin. The handcrafted descriptors were those representatives of multiscale and multidimensional fractal techniques (fractal dimension, lacunarity and percolation) applied to quantify the histological images with the corresponding representations via explainable artificial intelligence (xAI) approaches. The deep-learned features were obtained from different convolutional neural networks (DenseNet-121, EfficientNet-b2, Inception-V3, ResNet-50 and VGG-19). The descriptors were investigated through different associations. The most relevant combinations, defined through a ranking algorithm, were analyzed via a heterogeneous ensemble of classifiers with the support vector machine, naive Bayes, random forest and K-nearest neighbors algorithms. The proposed scheme was applied to histological samples representative of breast cancer, colorectal cancer, oral dysplasia and liver tissue. The best results were accuracy rates of 94.83% to 100%, with the identification of pattern ensembles for classifying multiple histological images. The computational scheme indicated solutions exploring a reduced number of features (a maximum of 25 descriptors) and with better performance values than those observed in the literature. The presented information in this study is useful to complement and improve the development of computer-aided diagnosis focused on histological images.
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Affiliation(s)
- Leonardo H. da Costa Longo
- Department of Computer Science and Statistics (DCCE), São Paulo State University (UNESP), Rua Cristóvão Colombo, 2265, São José do Rio Preto 15054-000, SP, Brazil
| | - Guilherme F. Roberto
- Department of Informatics Engineering, Faculty of Engineering, University of Porto, Dr. Roberto Frias, sn, 4200-465 Porto, Portugal;
| | - Thaína A. A. Tosta
- Science and Technology Institute, Federal University of São Paulo (UNIFESP), Avenida Cesare Mansueto Giulio Lattes, 1201, São José dos Campos 12247-014, SP, Brazil;
| | - Paulo R. de Faria
- Department of Histology and Morphology, Institute of Biomedical Science, Federal University of Uberlândia (UFU), Av. Amazonas, S/N, Uberlândia 38405-320, MG, Brazil;
| | - Adriano M. Loyola
- Area of Oral Pathology, School of Dentistry, Federal University of Uberlândia (UFU), R. Ceará—Umuarama, Uberlândia 38402-018, MG, Brazil; (A.M.L.)
| | - Sérgio V. Cardoso
- Area of Oral Pathology, School of Dentistry, Federal University of Uberlândia (UFU), R. Ceará—Umuarama, Uberlândia 38402-018, MG, Brazil; (A.M.L.)
| | - Adriano B. Silva
- Faculty of Computer Science (FACOM), Federal University of Uberlândia (UFU), Avenida João Naves de Ávila 2121, Bl.B, Uberlândia 38400-902, MG, Brazil
| | - Marcelo Z. do Nascimento
- Faculty of Computer Science (FACOM), Federal University of Uberlândia (UFU), Avenida João Naves de Ávila 2121, Bl.B, Uberlândia 38400-902, MG, Brazil
| | - Leandro A. Neves
- Department of Computer Science and Statistics (DCCE), São Paulo State University (UNESP), Rua Cristóvão Colombo, 2265, São José do Rio Preto 15054-000, SP, Brazil
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2
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Zou K, Wang S, Wang Z, Zou H, Yang F. Dual-Signal Feature Spaces Map Protein Subcellular Locations Based on Immunohistochemistry Image and Protein Sequence. SENSORS (BASEL, SWITZERLAND) 2023; 23:9014. [PMID: 38005402 PMCID: PMC10675401 DOI: 10.3390/s23229014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 10/29/2023] [Accepted: 11/01/2023] [Indexed: 11/26/2023]
Abstract
Protein is one of the primary biochemical macromolecular regulators in the compartmental cellular structure, and the subcellular locations of proteins can therefore provide information on the function of subcellular structures and physiological environments. Recently, data-driven systems have been developed to predict the subcellular location of proteins based on protein sequence, immunohistochemistry (IHC) images, or immunofluorescence (IF) images. However, the research on the fusion of multiple protein signals has received little attention. In this study, we developed a dual-signal computational protocol by incorporating IHC images into protein sequences to learn protein subcellular localization. Three major steps can be summarized as follows in this protocol: first, a benchmark database that includes 281 proteins sorted out from 4722 proteins of the Human Protein Atlas (HPA) and Swiss-Prot database, which is involved in the endoplasmic reticulum (ER), Golgi apparatus, cytosol, and nucleoplasm; second, discriminative feature operators were first employed to quantitate protein image-sequence samples that include IHC images and protein sequence; finally, the feature subspace of different protein signals is absorbed to construct multiple sub-classifiers via dimensionality reduction and binary relevance (BR), and multiple confidence derived from multiple sub-classifiers is adopted to decide subcellular location by the centralized voting mechanism at the decision layer. The experimental results indicated that the dual-signal model embedded IHC images and protein sequences outperformed the single-signal models with accuracy, precision, and recall of 75.41%, 80.38%, and 74.38%, respectively. It is enlightening for further research on protein subcellular location prediction under multi-signal fusion of protein.
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Affiliation(s)
- Kai Zou
- School of Communications and Electronics, Jiangxi Science and Technology Normal University, Nanchang 330038, China
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Simeng Wang
- School of Communications and Electronics, Jiangxi Science and Technology Normal University, Nanchang 330038, China
| | - Ziqian Wang
- School of Communications and Electronics, Jiangxi Science and Technology Normal University, Nanchang 330038, China
| | - Hongliang Zou
- School of Communications and Electronics, Jiangxi Science and Technology Normal University, Nanchang 330038, China
| | - Fan Yang
- School of Communications and Electronics, Jiangxi Science and Technology Normal University, Nanchang 330038, China
- Artificial Intelligence and Bioinformation Cognition Laboratory, Jiangxi Science and Technology Normal University, Nanchang 330038, China
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3
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Wu Y, Li Y, Xiong X, Liu X, Lin B, Xu B. Recent advances of pathomics in colorectal cancer diagnosis and prognosis. Front Oncol 2023; 13:1094869. [PMID: 37538112 PMCID: PMC10396402 DOI: 10.3389/fonc.2023.1094869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 06/13/2023] [Indexed: 08/05/2023] Open
Abstract
Colorectal cancer (CRC) is one of the most common malignancies, with the third highest incidence and the second highest mortality in the world. To improve the therapeutic outcome, the risk stratification and prognosis predictions would help guide clinical treatment decisions. Achieving these goals have been facilitated by the fast development of artificial intelligence (AI) -based algorithms using radiological and pathological data, in combination with genomic information. Among them, features extracted from pathological images, termed pathomics, are able to reflect sub-visual characteristics linking to better stratification and prediction of therapeutic responses. In this paper, we review recent advances in pathological image-based algorithms in CRC, focusing on diagnosis of benign and malignant lesions, micro-satellite instability, as well as prediction of neoadjuvant chemoradiotherapy and the prognosis of CRC patients.
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Affiliation(s)
- Yihan Wu
- School of Medicine, Chongqing University, Chongqing, China
- Chongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital, Chongqing, China
| | - Yi Li
- Chongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital, Chongqing, China
- Bioengineering College, Chongqing University, Chongqing, China
| | - Xiaomin Xiong
- Chongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital, Chongqing, China
- Bioengineering College, Chongqing University, Chongqing, China
| | - Xiaohua Liu
- Bioengineering College, Chongqing University, Chongqing, China
| | - Bo Lin
- Chongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital, Chongqing, China
| | - Bo Xu
- School of Medicine, Chongqing University, Chongqing, China
- Chongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital, Chongqing, China
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4
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Hu JX, Yang Y, Xu YY, Shen HB. GraphLoc: a graph neural network model for predicting protein subcellular localization from immunohistochemistry images. Bioinformatics 2022; 38:4941-4948. [DOI: 10.1093/bioinformatics/btac634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 09/07/2022] [Accepted: 09/15/2022] [Indexed: 11/14/2022] Open
Abstract
Abstract
Motivation
Recognition of protein subcellular distribution patterns and identification of location biomarker proteins in cancer tissues are important for understanding protein functions and related diseases. Immunohistochemical (IHC) images enable visualizing the distribution of proteins at the tissue level, providing an important resource for the protein localization studies. In the past decades, several image-based protein subcellular location prediction methods have been developed, but the prediction accuracies still have much space to improve due to the complexity of protein patterns resulting from multi-label proteins and variation of location patterns across cell types or states.
Results
Here, we propose a multi-label multi-instance model based on deep graph convolutional neural networks, GraphLoc, to recognize protein subcellular location patterns. GraphLoc builds a graph of multiple IHC images for one protein, learns protein-level representations by graph convolutions, and predicts multi-label information by a dynamic threshold method. Our results show that GraphLoc is a promising model for image-based protein subcellular location prediction with model interpretability. Furthermore, we apply GraphLoc to the identification of candidate location biomarkers and potential members for protein networks. A large portion of the predicted results have supporting evidence from the existing literatures and the new candidates also provide guidance for further experimental screening.
Availability
The dataset and code are available at: www.csbio.sjtu.edu.cn/bioinf/GraphLoc.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jin-Xian Hu
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing , Ministry of Education of China, Shanghai 200240, China
| | - Yang Yang
- Shanghai Jiao Tong University Department of Computer Science and Engineering, Center for Brain-Like Computing and Machine Intelligence, , Shanghai 200240, China
| | - Ying-Ying Xu
- Southern Medical University School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, , Guangzhou 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University , Guangzhou 510515, China
| | - Hong-Bin Shen
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing , Ministry of Education of China, Shanghai 200240, China
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5
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Ullah M, Hadi F, Song J, Yu DJ. PScL-DDCFPred: an ensemble deep learning-based approach for characterizing multiclass subcellular localization of human proteins from bioimage data. Bioinformatics 2022; 38:4019-4026. [PMID: 35771606 PMCID: PMC9890309 DOI: 10.1093/bioinformatics/btac432] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 06/03/2022] [Accepted: 06/28/2022] [Indexed: 02/04/2023] Open
Abstract
MOTIVATION Characterization of protein subcellular localization has become an important and long-standing task in bioinformatics and computational biology, which provides valuable information for elucidating various cellular functions of proteins and guiding drug design. RESULTS Here, we develop a novel bioimage-based computational approach, termed PScL-DDCFPred, to accurately predict protein subcellular localizations in human tissues. PScL-DDCFPred first extracts multiview image features, including global and local features, as base or pure features; next, it applies a new integrative feature selection method based on stepwise discriminant analysis and generalized discriminant analysis to identify the optimal feature sets from the extracted pure features; Finally, a classifier based on deep neural network (DNN) and deep-cascade forest (DCF) is established. Stringent 10-fold cross-validation tests on the new protein subcellular localization training dataset, constructed from the human protein atlas databank, illustrates that PScL-DDCFPred achieves a better performance than several existing state-of-the-art methods. Moreover, the independent test set further illustrates the generalization capability and superiority of PScL-DDCFPred over existing predictors. In-depth analysis shows that the excellent performance of PScL-DDCFPred can be attributed to three critical factors, namely the effective combination of the DNN and DCF models, complementarity of global and local features, and use of the optimal feature sets selected by the integrative feature selection algorithm. AVAILABILITY AND IMPLEMENTATION https://github.com/csbio-njust-edu/PScL-DDCFPred. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Matee Ullah
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Fazal Hadi
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Jiangning Song
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
- Monash Data Futures Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Dong-Jun Yu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
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6
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Xue MQ, Zhu XL, Wang G, Xu YY. DULoc: quantitatively unmixing protein subcellular location patterns in immunofluorescence images based on deep learning features. Bioinformatics 2022; 38:827-833. [PMID: 34694372 DOI: 10.1093/bioinformatics/btab730] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 09/13/2021] [Accepted: 10/20/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Knowledge of subcellular locations of proteins is of great significance for understanding their functions. The multi-label proteins that simultaneously reside in or move between more than one subcellular structure usually involve with complex cellular processes. Currently, the subcellular location annotations of proteins in most studies and databases are descriptive terms, which fail to capture the protein amount or fractions across different locations. This highly limits the understanding of complex spatial distribution and functional mechanism of multi-label proteins. Thus, quantitatively analyzing the multiplex location patterns of proteins is an urgent and challenging task. RESULTS In this study, we developed a deep-learning-based pattern unmixing pipeline for protein subcellular localization (DULoc) to quantitatively estimate the fractions of proteins localizing in different subcellular compartments from immunofluorescence images. This model used a deep convolutional neural network to construct feature representations, and combined multiple nonlinear decomposing algorithms as the pattern unmixing method. Our experimental results showed that the DULoc can achieve over 0.93 correlation between estimated and true fractions on both real and synthetic datasets. In addition, we applied the DULoc method on the images in the human protein atlas database on a large scale, and showed that 70.52% of proteins can achieve consistent location orders with the database annotations. AVAILABILITY AND IMPLEMENTATION The datasets and code are available at: https://github.com/PRBioimages/DULoc. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Min-Qi Xue
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China
| | - Xi-Liang Zhu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China
| | - Ge Wang
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China
| | - Ying-Ying Xu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China
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7
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Deep localization of subcellular protein structures from fluorescence microscopy images. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06715-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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8
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Wang G, Xue MQ, Shen HB, Xu YY. Learning protein subcellular localization multi-view patterns from heterogeneous data of imaging, sequence and networks. Brief Bioinform 2022; 23:6499983. [PMID: 35018423 DOI: 10.1093/bib/bbab539] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 11/03/2021] [Accepted: 11/20/2021] [Indexed: 11/13/2022] Open
Abstract
Location proteomics seeks to provide automated high-resolution descriptions of protein location patterns within cells. Many efforts have been undertaken in location proteomics over the past decades, thereby producing plenty of automated predictors for protein subcellular localization. However, most of these predictors are trained solely from high-throughput microscopic images or protein amino acid sequences alone. Unifying heterogeneous protein data sources has yet to be exploited. In this paper, we present a pipeline called sequence, image, network-based protein subcellular locator (SIN-Locator) that constructs a multi-view description of proteins by integrating multiple data types including images of protein expression in cells or tissues, amino acid sequences and protein-protein interaction networks, to classify the patterns of protein subcellular locations. Proteins were encoded by both handcrafted features and deep learning features, and multiple combining methods were implemented. Our experimental results indicated that optimal integrations can considerately enhance the classification accuracy, and the utility of SIN-Locator has been demonstrated through applying to new released proteins in the human protein atlas. Furthermore, we also investigate the contribution of different data sources and influence of partial absence of data. This work is anticipated to provide clues for reconciliation and combination of multi-source data for protein location analysis.
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Affiliation(s)
- Ge Wang
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China
| | - Min-Qi Xue
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China
| | - Hong-Bin Shen
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China.,School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Ying-Ying Xu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China
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Ullah M, Han K, Hadi F, Xu J, Song J, Yu DJ. PScL-HDeep: image-based prediction of protein subcellular location in human tissue using ensemble learning of handcrafted and deep learned features with two-layer feature selection. Brief Bioinform 2021; 22:bbab278. [PMID: 34337652 PMCID: PMC8574991 DOI: 10.1093/bib/bbab278] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Revised: 06/30/2021] [Accepted: 07/01/2021] [Indexed: 01/17/2023] Open
Abstract
Protein subcellular localization plays a crucial role in characterizing the function of proteins and understanding various cellular processes. Therefore, accurate identification of protein subcellular location is an important yet challenging task. Numerous computational methods have been proposed to predict the subcellular location of proteins. However, most existing methods have limited capability in terms of the overall accuracy, time consumption and generalization power. To address these problems, in this study, we developed a novel computational approach based on human protein atlas (HPA) data, referred to as PScL-HDeep, for accurate and efficient image-based prediction of protein subcellular location in human tissues. We extracted different handcrafted and deep learned (by employing pretrained deep learning model) features from different viewpoints of the image. The step-wise discriminant analysis (SDA) algorithm was applied to generate the optimal feature set from each original raw feature set. To further obtain a more informative feature subset, support vector machine-based recursive feature elimination with correlation bias reduction (SVM-RFE + CBR) feature selection algorithm was applied to the integrated feature set. Finally, the classification models, namely support vector machine with radial basis function (SVM-RBF) and support vector machine with linear kernel (SVM-LNR), were learned on the final selected feature set. To evaluate the performance of the proposed method, a new gold standard benchmark training dataset was constructed from the HPA databank. PScL-HDeep achieved the maximum performance on 10-fold cross validation test on this dataset and showed a better efficacy over existing predictors. Furthermore, we also illustrated the generalization ability of the proposed method by conducting a stringent independent validation test.
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Affiliation(s)
- Matee Ullah
- Nanjing University of Science and Technology, China
| | - Ke Han
- School of Computer Science and Engineering, Nanjing University of Science and Technology, China
| | - Fazal Hadi
- Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan
| | - Jian Xu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, China
| | - Jiangning Song
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Australia
| | - Dong-Jun Yu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, China
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10
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Hu JX, Yang Y, Xu YY, Shen HB. Incorporating label correlations into deep neural networks to classify protein subcellular location patterns in immunohistochemistry images. Proteins 2021; 90:493-503. [PMID: 34546597 DOI: 10.1002/prot.26244] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 03/16/2021] [Accepted: 09/13/2021] [Indexed: 12/17/2022]
Abstract
Analysis of protein subcellular localization is a critical part of proteomics. In recent years, as both the number and quality of microscopic images are increasing rapidly, many automated methods, especially convolutional neural networks (CNN), have been developed to predict protein subcellular location(s) based on bioimages, but their performance always suffers from some inherent properties of the problem. First, many microscopic images have non-informative or noisy sections, like unstained stroma and unspecific background, which affect the extraction of protein expression information. Second, the patterns of protein subcellular localization are very complex, as a lot of proteins locate in more than one compartment. In this study, we propose a new label-correlation enhanced deep neural network, laceDNN, to classify the subcellular locations of multi-label proteins from immunohistochemistry images. The model uses small representative patches as input to alleviate the image noise issue, and its backbone is a hybrid architecture of CNN and recurrent neural network, where the former network extracts representative image features and the latter learns the organelle dependency relationships. Our experimental results indicate that the proposed model can improve the performance of multi-label protein subcellular classification.
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Affiliation(s)
- Jin-Xian Hu
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China
| | - Yang Yang
- Department of Computer Science and Engineering, Center for Brain-Like Computing and Machine Intelligence, Shanghai Jiao Tong University, Shanghai, China
| | - Ying-Ying Xu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
| | - Hong-Bin Shen
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China
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11
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Kang H, Kang S. A stacking ensemble classifier with handcrafted and convolutional features for wafer map pattern classification. COMPUT IND 2021. [DOI: 10.1016/j.compind.2021.103450] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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12
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Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification. J Imaging 2021; 7:jimaging7030051. [PMID: 34460707 PMCID: PMC8321410 DOI: 10.3390/jimaging7030051] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Revised: 02/16/2021] [Accepted: 02/26/2021] [Indexed: 02/06/2023] Open
Abstract
In recent years, automatic tissue phenotyping has attracted increasing interest in the Digital Pathology (DP) field. For Colorectal Cancer (CRC), tissue phenotyping can diagnose the cancer and differentiate between different cancer grades. The development of Whole Slide Images (WSIs) has provided the required data for creating automatic tissue phenotyping systems. In this paper, we study different hand-crafted feature-based and deep learning methods using two popular multi-classes CRC-tissue-type databases: Kather-CRC-2016 and CRC-TP. For the hand-crafted features, we use two texture descriptors (LPQ and BSIF) and their combination. In addition, two classifiers are used (SVM and NN) to classify the texture features into distinct CRC tissue types. For the deep learning methods, we evaluate four Convolutional Neural Network (CNN) architectures (ResNet-101, ResNeXt-50, Inception-v3, and DenseNet-161). Moreover, we propose two Ensemble CNN approaches: Mean-Ensemble-CNN and NN-Ensemble-CNN. The experimental results show that the proposed approaches outperformed the hand-crafted feature-based methods, CNN architectures and the state-of-the-art methods in both databases.
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13
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Atila Ü, Baydilli YY, Sehirli E, Turan MK. Classification of DNA damages on segmented comet assay images using convolutional neural network. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 186:105192. [PMID: 31733518 DOI: 10.1016/j.cmpb.2019.105192] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Revised: 11/04/2019] [Accepted: 11/06/2019] [Indexed: 05/22/2023]
Abstract
BACKGROUND AND OBJECTIVE Identification and quantification of DNA damage is a very significant subject in biomedical research area which still needs more robust and effective methods. One of the cheapest, easy to use and most successful method for DNA damage analyses is comet assay. In this study, performance of Convolutional Neural Network was examined on quantification of DNA damage using comet assay images and was compared to other methods in the literature. METHODS 796 single comet grayscale images with 170 x 170 resolution labeled by an expert and classified into 4 classes each having approximately 200 samples as G0 (healthy), G1 (poorly defective), G2 (defective) and G3 (very defective) were utilized. 120 samples were used as test dataset and the rest were used in data augmentation process to achieve better performance with training of Convolutional Neural Network. The augmented data having a total of 9995 images belonging to four classes were used as network training data set. RESULTS The proposed model, which was not dependent to pre-processing parameters of image processing for DNA damage classification, was able to classify comet images into 4 classes with an overall accuracy rate of 96.1%. CONCLUSIONS This paper primarily focuses on features and usage of Convolutional Neural Network as a novel method to classify comet objects on segmented comet assay images.
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Affiliation(s)
- Ümit Atila
- Department of Computer Engineering, Faculty of Engineering, Karabuk University, Karabuk, Turkey
| | - Yusuf Yargı Baydilli
- Department of Computer Engineering, Faculty of Engineering, Karabuk University, Karabuk, Turkey
| | - Eftal Sehirli
- Department of Medical Engineering, Faculty of Engineering, Karabuk University, Karabuk, Turkey.
| | - Muhammed Kamil Turan
- Department of Medical Biology, Faculty of Medicine, Karabuk University, Karabuk, Turkey
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14
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Nanni L, Lumini A, Ghidoni S, Maguolo G. Stochastic Selection of Activation Layers for Convolutional Neural Networks. SENSORS 2020; 20:s20061626. [PMID: 32183334 PMCID: PMC7147370 DOI: 10.3390/s20061626] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 03/11/2020] [Accepted: 03/12/2020] [Indexed: 11/16/2022]
Abstract
In recent years, the field of deep learning has achieved considerable success in pattern recognition, image segmentation, and many other classification fields. There are many studies and practical applications of deep learning on images, video, or text classification. Activation functions play a crucial role in discriminative capabilities of the deep neural networks and the design of new "static" or "dynamic" activation functions is an active area of research. The main difference between "static" and "dynamic" functions is that the first class of activations considers all the neurons and layers as identical, while the second class learns parameters of the activation function independently for each layer or even each neuron. Although the "dynamic" activation functions perform better in some applications, the increased number of trainable parameters requires more computational time and can lead to overfitting. In this work, we propose a mixture of "static" and "dynamic" activation functions, which are stochastically selected at each layer. Our idea for model design is based on a method for changing some layers along the lines of different functional blocks of the best performing CNN models, with the aim of designing new models to be used as stand-alone networks or as a component of an ensemble. We propose to replace each activation layer of a CNN (usually a ReLU layer) by a different activation function stochastically drawn from a set of activation functions: in this way, the resulting CNN has a different set of activation function layers.
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Affiliation(s)
- Loris Nanni
- Department of Information Enginering, University of Padua, viale Gradenigo 6, 35131 Padua, Italy; (S.G.); (G.M.)
- Correspondence:
| | - Alessandra Lumini
- DISI, Università di Bologna, Via dell’università 50, 47521 Cesena, Italy;
| | - Stefano Ghidoni
- Department of Information Enginering, University of Padua, viale Gradenigo 6, 35131 Padua, Italy; (S.G.); (G.M.)
| | - Gianluca Maguolo
- Department of Information Enginering, University of Padua, viale Gradenigo 6, 35131 Padua, Italy; (S.G.); (G.M.)
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15
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Comparing convolutional neural networks and preprocessing techniques for HEp-2 cell classification in immunofluorescence images. Comput Biol Med 2020; 116:103542. [DOI: 10.1016/j.compbiomed.2019.103542] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 11/12/2019] [Accepted: 11/12/2019] [Indexed: 01/10/2023]
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16
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Local bit-plane decoded convolutional neural network features for biomedical image retrieval. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04279-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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