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Divide-and-Attention Network for HE-Stained Pathological Image Classification
2022
Biology
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(HE)-stained pathological image classification. ...
Inspired by the clinical experience that decomposing a pathological image into different components is beneficial for diagnosis, in this paper, we propose a Divide-and-Attention Network (DANet) for Hematoxylin-and-Eosin ...
Decomposition-and-Fusion Network for HE-Stained Pathological Image Classification. In International Conference on Intelligent Computing; Springer: Cham, Switzerland, 2021; pp. 198-207. ...
doi:10.3390/biology11070982
pmid:36101363
pmcid:PMC9311575
fatcat:c3t7mudihvb35f2d52fv6itcf4
Evaluating Cancer-Related Biomarkers Based on Pathological Images: A Systematic Review
2021
Frontiers in Oncology
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Therefore, it is necessary to summarize the current process of processing pathological images and key steps and methods used in each process, including: (1) pre-processing of pathological images, (2) image ...
Pathological image analysis is an essential tool in medical research, disease diagnosis and treatment, functioning by extracting important physiological and pathological information or knowledge from medical ...
AUTHOR CONTRIBUTIONS GT and XShi designed the project. XX, XW, and XShi searched literatures and wrote the manuscript. YL, JY, YW, LL, XSun, PB, and BH revised the manuscript. ...
doi:10.3389/fonc.2021.763527
pmid:34900711
pmcid:PMC8660076
fatcat:g7kyyexwo5evxmrlxg3l3hjqci
Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions
[article]
2024
arXiv
pre-print
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2023 pre-print arXiv:2304.06662v3 fatcat:w2bqc2lqunhrfmx456tjqnxguqOther Versions
This paper provides an extensive review of deep learning-based breast cancer imaging research, covering studies on mammogram, ultrasound, magnetic resonance imaging, and digital pathology images over the ...
Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging. ...
He et al. (2020) CL TCGA, ID ST-Net (ImageNet pre-trained DenseNet-121) for the prediction of local gene expression from H&E-stained histopathology images. ...
arXiv:2304.06662v4
fatcat:t5nvpybawjhfhiw4h2bekozo74
Breast Cancer Image Classification via Multi-network Features and Dual-network Orthogonal Low-rank Learning
2020
IEEE Access
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Histopathological image analysis is an important technique for early diagnosis and detection of breast cancer in clinical practice. ...
INDEX TERMS Breast cancer image classification, deep convolutional neural network, multi-network features, low-rank learning, ensemble support vector machine. ...
dramatically alleviate the uneven staining issue of pathological images. ...
doi:10.1109/access.2020.2964276
fatcat:7u2xjgvheba3xnkdjcjepqyzwa
A State-of-the-Art Review for Gastric Histopathology Image Analysis Approaches and Future Development
2021
BioMed Research International
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Firstly, the paper summarizes the image preprocessing methods, then introduces the methods of feature extraction, and then generalizes the existing segmentation and classification techniques. ...
In this review, the CAD technique on pathological images of gastric cancer is summarized. ...
Acknowledgments The authors thank Miss Zixian Li and Mr. Guoxian Li for their important discussion. ...
doi:10.1155/2021/6671417
pmid:34258279
pmcid:PMC8257332
fatcat:nmcxblueazblvbd6753cmtqpgm
Histo-CADx: duo cascaded fusion stages for breast cancer diagnosis from histopathological images
2021
PeerJ Computer Science
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It may lead to irreversible complications and even death due to late diagnosis and treatment. The pathological analysis is considered the gold standard for BC detection, but it is a challenging task. ...
Furthermore, using the auto-encoder for the fusion process has reduced the computation cost of the system. ...
Lately, numerous CNN-based methods for automatic and accurate classification of BC pathological images were established for the ICIAR2018 challenge (Aresta et al., 2019) . ...
doi:10.7717/peerj-cs.493
pmid:33987459
pmcid:PMC8093954
fatcat:5bmyenkh65cthluu6kzp2kdhee
A Polarization-Imaging-Based Machine Learning Framework for Quantitative Pathological Diagnosis of Cervical Precancerous Lesions
2021
IEEE Transactions on Medical Imaging
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The proposed framework applies routine image-analysis technology to identify the macro-structure and segment the target region in H&E-stained pathological images, and then employs emerging polarization ...
method to extract the micro-structure information of the target region, which intends to expand the boundary of the current image-heavy digital pathology, bringing new possibilities for quantitative medical ...
ACKNOWLEDGMENT We thank Zhi Wang and Ruqi Huang for helpful feedback on the manuscript. ...
doi:10.1109/tmi.2021.3097200
pmid:34260351
fatcat:vrbru3h3pneuxfb3a6n4o2kmzm
Progress of Machine Vision in the Detection of Cancer Cells in Histopathology
2022
IEEE Access
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disadvantages of existing methods in image preprocessing, segmentation, feature extraction and recognition. ...
Finally, research on the detection methods of histopathological cancer cells is reviewed and prospected, and future development trends are predicted to provide guidance for follow-up research. ...
[94] proposed to use capsule network for oral cancer classification. ...
doi:10.1109/access.2022.3161575
fatcat:uzj3rxfpqjg5xpy2sjdjjk2j5i
Digital pathology and computational image analysis in nephropathology
2020
Nature Reviews Nephrology
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The emergence of digital pathology - an image-based environment for the acquisition, management and interpretation of pathology information supported by computational techniques for data extraction and ...
Although these novel approaches have already advanced the detection, classification, and prognostication of diseases in the fields of radiology and oncology, renal pathology is just entering the digital ...
Computational pathology The science that includes big data generation and analysis, image processing, data mining and data fusion of digital pathology data. ...
doi:10.1038/s41581-020-0321-6
pmid:32848206
pmcid:PMC7447970
fatcat:aezfryzpzjconpl6de7vdw53h4
Classification of Hematoxylin and Eosin-Stained Breast Cancer Histology Microscopy Images Using Transfer Learning with EfficientNets
2021
Computational Intelligence and Neuroscience
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This research investigates the application of the EfficientNet architecture for the classification of hematoxylin and eosin-stained breast cancer histology images provided by the ICIAR2018 dataset. ...
The outcome of this approach reveals that the EfficientNet-B2 model yielded an accuracy and sensitivity of 98.33% using Reinhard stain normalization method on the training images and an accuracy and sensitivity ...
For the classification of hematoxylin and eosin-stained breast cancer histology images, both Araújo et al. [5] and Vo et al. ...
doi:10.1155/2021/5580914
pmid:33897774
pmcid:PMC8052174
fatcat:ar4yiht5hzbg5mlqp4mly6vyxi
Deep Learning Models Combining for Breast Cancer Histopathology Image Classification
2021
International Journal of Computational Intelligence Systems
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The whole image classification overall accuracy reaches 100% by majority vote and 95% by maximum probability fusion decision. ...
The overall accuracy for the sub-image classification is 97.29% and for the carcinoma cases the sensitivity achieved 99.58%. ...
ACKNOWLEDGMENTS The authors would like to thank the Center for Promising Research in Social Research and Women's Studies Deanship of Scientific Research at Princess Nourah University for funding this Project ...
doi:10.2991/ijcis.d.210301.002
fatcat:3url2cppfbf27n25ec2fbivx4a
Classification of histopathological breast cancer images using iterative VMD aided Zernike moments & textural signatures
[article]
2018
arXiv
pre-print
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In this paper we present a novel method for an automated diagnosis of breast carcinoma through multilevel iterative variational mode decomposition (VMD) and textural features encompassing Zernaike moments ...
ReliefF algorithm is used to select the discriminatory features and statistically most significant features are fed to squares support vector machine (SVM) for classification. ...
ACKNOWLEDGEMENT The authors would like to thank Fabio Alexandre Spanhol for providing an exhaustive and inclusive discussion and standard baseline results of the BreaKHis dataset. ...
arXiv:1801.04880v1
fatcat:gvaqncd2fvcwpikxfkjrvwe544
Registration and fusion of retinal images-an evaluation study
2003
IEEE Transactions on Medical Imaging
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retinal network. ...
We present the results of a study on the application of registration and pixel-level fusion techniques to retinal images. ...
Boucher from the Maisonneuve-Rosemont Hospital (Montreal, QC, Canada) for providing some of the images and clinical inputs. ...
doi:10.1109/tmi.2003.812263
pmid:12846435
fatcat:vexrhuop7zhsblt37kaws4yvom
Deep Learning for Whole-Slide Tissue Histopathology Classification: A Comparative Study in the Identification of Dysplastic and Non-Dysplastic Barrett's Esophagus
2020
Journal of Personalized Medicine
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We performed a comparative study to elucidate the characteristics and behaviors of different deep learning-based feature representation approaches for the WSI-based diagnosis of diseased esophageal architectures ...
The results showed that if appropriate settings are chosen, the unsupervised feature representation approach is capable of extracting more relevant image features from WSIs to classify and locate the precursors ...
Patch-based convolutional neural network
for whole slide tissue image classification. ...
doi:10.3390/jpm10040141
pmid:32977465
pmcid:PMC7711456
fatcat:j73keotvufgq7nja26vtlbqq7e
Review on Brain Tumor Segmentation and Classification Techniques
2017
International Journal of Engineering Research and
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, but the manual classification of the MR images is the challenging and time consuming task. ...
The main aim of this research and review paper is to explore the existing segmentation and classification techniques in the medical image processing. ...
networks (ANN's) for the classification and segmentation of magnetic resonance (MR) images of the human brain. ...
doi:10.17577/ijertv6is110008
fatcat:lh6yklz5cfen7nwsgjwblalz4q
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