Detection of mitotic cells in breast cancer histopathological images using deep versus handcrafted features

Sigirci, Ibrahim Onur; Albayrak, Abdülkadir; Bilgin, Gökhan

doi:10.1007/s11042-021-10539-2

Cited by 28 publications

(7 citation statements)

References 43 publications

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“…Therefore, until now, various approaches have been presented for cell phenotype classification based on machine learning and computer vision techniques. In this scope, most of the efficient approaches are categorized into two main strategies [ 1 , 3 ]: Handcrafted-based features in joint of machine learning techniques Deep learning-based methods …”

Section: Introductionmentioning

confidence: 99%

Cell Phenotype Classification Based on Joint of Texture Information and Multilayer Feature Extraction in DenseNet

Fekri-Ershad

Al-Imari²,

Hamad³

et al. 2022

Computational Intelligence and Neuroscience

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Cell phenotype classification is a critical task in many medical applications, such as protein localization, gene effect identification, and cancer diagnosis in some types. Fluorescence imaging is the most efficient tool to analyze the biological characteristics of cells. So cell phenotype classification in fluorescence microscopy images has received increased attention from scientists in the last decade. The visible structures of cells are usually different in terms of shape, texture, relationship between intensities, etc. In this scope, most of the presented approaches use one type or joint of low-level and high-level features. In this paper, a new approach is proposed based on a combination of low-level and high-level features. An improved version of local quinary patterns is used to extract low-level texture features. Also, an innovative multilayer deep feature extraction method is performed to extract high-level features from DenseNet. In this respect, an output feature map of dense blocks is entered in a separate way to pooling and flatten layers, and finally, feature vectors are concatenated. The performance of the proposed approach is evaluated on the benchmark dataset 2D-HeLa in terms of accuracy. Also, the proposed approach is compared with state-of-the-art methods in terms of classification accuracy. Comparison of results demonstrates higher performance of the proposed approach in comparison with some efficient methods.

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Section: Introductionmentioning

confidence: 99%

Cell Phenotype Classification Based on Joint of Texture Information and Multilayer Feature Extraction in DenseNet

Fekri-Ershad

Al-Imari²,

Hamad³

et al. 2022

Computational Intelligence and Neuroscience

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show abstract

“…In the same MITOS-ATYPIA 2014 challenge, the previous record was broken this way with a new F-score of

96

. 404 Although one cannot compare these two works directly as they use different classifier heads and dataset balancing methods, one can argue that the optimal choice of approaches from deep learning, classical ML, and different modalities should depend on the situation. Multi-modal approaches are gaining traction in CPath for specific problems, especially where useful additional data is available.…”

Section: Model Learning For Cpathmentioning

confidence: 99%

Computational Pathology: A Survey Review and The Way Forward

Hosseini,

Bejnordi,

Trinh

et al. 2024

Journal of Pathology Informatics

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“…In medical image analysis, detection aims to locate areas of interest in tissue slices [28][29][30][31][32][33][34][35][36][37][38][39][40].…”

Section: Detection Of Breast Lesionsmentioning

confidence: 99%

Application of Deep Learning in Histopathology Images of Breast Cancer: A Review

Zhao

Zhang

et al. 2022

Micromachines

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With the development of artificial intelligence technology and computer hardware functions, deep learning algorithms have become a powerful auxiliary tool for medical image analysis. This study was an attempt to use statistical methods to analyze studies related to the detection, segmentation, and classification of breast cancer in pathological images. After an analysis of 107 articles on the application of deep learning to pathological images of breast cancer, this study is divided into three directions based on the types of results they report: detection, segmentation, and classification. We introduced and analyzed models that performed well in these three directions and summarized the related work from recent years. Based on the results obtained, the significant ability of deep learning in the application of breast cancer pathological images can be recognized. Furthermore, in the classification and detection of pathological images of breast cancer, the accuracy of deep learning algorithms has surpassed that of pathologists in certain circumstances. Our study provides a comprehensive review of the development of breast cancer pathological imaging-related research and provides reliable recommendations for the structure of deep learning network models in different application scenarios.

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Detection of mitotic cells in breast cancer histopathological images using deep versus handcrafted features

Cited by 28 publications

References 43 publications

Cell Phenotype Classification Based on Joint of Texture Information and Multilayer Feature Extraction in DenseNet

Cell Phenotype Classification Based on Joint of Texture Information and Multilayer Feature Extraction in DenseNet

Computational Pathology: A Survey Review and The Way Forward

Application of Deep Learning in Histopathology Images of Breast Cancer: A Review

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