Deep learning‐based automated mitosis detection in histopathology images for breast cancer grading

Mathew, Tojo; Ajith, B.; Kini, Jyoti R.; Rajan, Jeny

doi:10.1002/ima.22703

Cited by 9 publications

(2 citation statements)

References 55 publications

(92 reference statements)

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“…Khan et al [12] introduced the SMDetector, a DL technique in which the dilated layers aim to reduce the size gap between the images and objects. Mathew et al [13] suggested a novel method based on a class imbalance phenomenon which is understood by the growth of mitotic cells in a context-preserving way. Eventually, the adapted CNN algorithm was employed for the classification of the candidate cells into target class labels.…”

Section: Related Workmentioning

confidence: 99%

Mitotic Nuclei Segmentation and Classification Using Chaotic Butterfly Optimization Algorithm with Deep Learning on Histopathology Images

AlGhamdi

2023

Biomimetics

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Histopathological grading of the tumors provides insights about the patient’s disease conditions, and it also helps in customizing the treatment plans. Mitotic nuclei classification involves the categorization and identification of nuclei in histopathological images based on whether they are undergoing the cell division (mitosis) process or not. This is an essential procedure in several research and medical contexts, especially in diagnosis and prognosis of cancer. Mitotic nuclei classification is a challenging task since the size of the nuclei is too small to observe, while the mitotic figures possess a different appearance as well. Automated calculation of mitotic nuclei is a stimulating one due to their great similarity to non-mitotic nuclei and their heteromorphic appearance. Both Computer Vision (CV) and Machine Learning (ML) approaches are used in the automated identification and the categorization of mitotic nuclei in histopathological images that endure the procedure of cell division (mitosis). With this background, the current research article introduces the mitotic nuclei segmentation and classification using the chaotic butterfly optimization algorithm with deep learning (MNSC-CBOADL) technique. The main objective of the MNSC-CBOADL technique is to perform automated segmentation and the classification of the mitotic nuclei. In the presented MNSC-CBOADL technique, the U-Net model is initially applied for the purpose of segmentation. Additionally, the MNSC-CBOADL technique applies the Xception model for feature vector generation. For the classification process, the MNSC-CBOADL technique employs the deep belief network (DBN) algorithm. In order to enhance the detection performance of the DBN approach, the CBOA is designed for the hyperparameter tuning model. The proposed MNSC-CBOADL system was validated through simulation using the benchmark database. The extensive results confirmed the superior performance of the proposed MNSC-CBOADL system in the classification of mitotic nuclei.

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Section: Related Workmentioning

confidence: 99%

Mitotic Nuclei Segmentation and Classification Using Chaotic Butterfly Optimization Algorithm with Deep Learning on Histopathology Images

AlGhamdi

2023

Biomimetics

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“…Binary Acc = 99% Avg. Multiclass Acc = 95% Mathew et al [ 289 ] 2022 Breast Cancer Cell Detection/Classification CNN ATYPIA MITOS F1 score = 61.91% Singh and Kumar [ 290 ] 2022 Classification Inception ResNet BHI BreakHis BHI: Acc = 85.21% BreakHis: Avg. Acc = 84% Mejbri et al [ 291 ] 2019 Tissue-level Segmentation DNNs Private U-Net: Dice = 86%, SegNet: Dice = 87%, FCN: Dice = 86%, DeepLab: Dice = 86% Guo et al [ 292 ] 2019 Cancer Regions Segmentation Transfer learning based on Inception-V3 and ResNet-101 Camelyon16 IOU = 80.4% AUC = 96.2% Priego-Torres et al [ 271 ] 2020 Tumor Segmentation CNN Private Acc = 95.62% IOU = 92.52% Budginaitė et al [ 293 ] 2021 Cell Nuclei Segmentation Micro-Net Pri...…”

Section: Artificial Intelligence In Medical Image Analysismentioning

confidence: 99%

The Role of Deep Learning in Advancing Breast Cancer Detection Using Different Imaging Modalities: A Systematic Review

Madani

Behzadi

Nabavi

2022

Cancers

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Breast cancer is among the most common and fatal diseases for women, and no permanent treatment has been discovered. Thus, early detection is a crucial step to control and cure breast cancer that can save the lives of millions of women. For example, in 2020, more than 65% of breast cancer patients were diagnosed in an early stage of cancer, from which all survived. Although early detection is the most effective approach for cancer treatment, breast cancer screening conducted by radiologists is very expensive and time-consuming. More importantly, conventional methods of analyzing breast cancer images suffer from high false-detection rates. Different breast cancer imaging modalities are used to extract and analyze the key features affecting the diagnosis and treatment of breast cancer. These imaging modalities can be divided into subgroups such as mammograms, ultrasound, magnetic resonance imaging, histopathological images, or any combination of them. Radiologists or pathologists analyze images produced by these methods manually, which leads to an increase in the risk of wrong decisions for cancer detection. Thus, the utilization of new automatic methods to analyze all kinds of breast screening images to assist radiologists to interpret images is required. Recently, artificial intelligence (AI) has been widely utilized to automatically improve the early detection and treatment of different types of cancer, specifically breast cancer, thereby enhancing the survival chance of patients. Advances in AI algorithms, such as deep learning, and the availability of datasets obtained from various imaging modalities have opened an opportunity to surpass the limitations of current breast cancer analysis methods. In this article, we first review breast cancer imaging modalities, and their strengths and limitations. Then, we explore and summarize the most recent studies that employed AI in breast cancer detection using various breast imaging modalities. In addition, we report available datasets on the breast-cancer imaging modalities which are important in developing AI-based algorithms and training deep learning models. In conclusion, this review paper tries to provide a comprehensive resource to help researchers working in breast cancer imaging analysis.

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Computational Synthesis of Histological Stains: A Step Toward Virtual Enhanced Digital Pathology

Salvi,

Michielli,

Salamone

et al. 2024

Int J Imaging Syst Tech

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Histological staining plays a crucial role in anatomic pathology for the analysis of biological tissues and the formulation of diagnostic reports. Traditional methods like hematoxylin and eosin (H&E) primarily offer morphological information but lack insight into functional details, such as the expression of biomarkers indicative of cellular activity. To overcome this limitation, we propose a computational approach to synthesize virtual immunohistochemical (IHC) stains from H&E input, transferring imaging features across staining domains. Our approach comprises two stages: (i) a multi‐stage registration framework ensuring precise alignment of cellular and subcellular structures between the source H&E and target IHC stains, and (ii) a deep learning‐based generative model which incorporates functional attributes from the target IHC stain by learning cell‐to‐cell mappings from paired training data. We evaluated our approach of virtual restaining H&E slides to simulate IHC staining for phospho‐histone H3, on inguinal lymph node and bladder tissues. Blind pathologist assessments and quantitative metrics validated the diagnostic quality of the synthetic slides. Notably, mitotic counts derived from synthetic images exhibited a strong correlation with physical staining. Moreover, global and stain‐specific metrics confirmed the high quality of the synthetic IHC images generated by our approach. This methodology represents an important advance in automated functional restaining, achieved through robust registration and a model trained on precisely paired H&E and IHC data to transfer functions cell‐by‐cell. Our approach forms the basis for multiparameter histology analysis and comprehensive cohort staining using only digitized H&E slides.

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Deep learning‐based automated mitosis detection in histopathology images for breast cancer grading

Cited by 9 publications

References 55 publications

Mitotic Nuclei Segmentation and Classification Using Chaotic Butterfly Optimization Algorithm with Deep Learning on Histopathology Images

Mitotic Nuclei Segmentation and Classification Using Chaotic Butterfly Optimization Algorithm with Deep Learning on Histopathology Images

The Role of Deep Learning in Advancing Breast Cancer Detection Using Different Imaging Modalities: A Systematic Review

Computational Synthesis of Histological Stains: A Step Toward Virtual Enhanced Digital Pathology

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