Automatic detection of mitosis cell in breast cancer histopathology images using genetic algorithm

Nateghi, Ramin; Danyali, Habibollah; SadeghHelfroush, Mohammad; Pour, Fattaneh Pourak

doi:10.1109/icbme.2014.7043883

Cited by 13 publications

(3 citation statements)

References 12 publications

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“…However, this method fails to demonstrate its robustness as region features used in the study may have significant variations among various datasets. Genetic algorithms for mitosis classification were proposed by Nateghi et al (2014). The authors used genetic optimization algorithms to eliminate potential non-mitosis from the histological image.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning for Semantic Segmentation vs. Classification in Computational Pathology: Application to Mitosis Analysis in Breast Cancer Grading

Jiménez

Racoceanu

2019

Front. Bioeng. Biotechnol.

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Existing computational approaches have not yet resulted in effective and efficient computer-aided tools that are used in pathologists' daily practice. Focusing on a computer-based qualification for breast cancer diagnosis, the present study proposes two deep learning architectures to efficiently and effectively detect and classify mitosis in a histopathological tissue sample. The first method consists of two parts, entailing a preprocessing of the digital histological image and a free-handcrafted-feature Convolutional Neural Network (CNN) used for binary classification. Results show that the methodology proposed can achieve 95% accuracy in testing, with an F1-score of 94.35%. This result is higher than the results using classical image processing techniques and also higher than the approaches combining CCNs with handcrafted features. The second approach is an end-to-end methodology using semantic segmentation. Results showed that this algorithm can achieve an accuracy higher than 95% in testing and an average Dice index of 0.6, higher than the existing results using CNNs (0.9 F1-score). Additionally, due to the semantic properties of the deep learning approach, an end-to-end deep learning framework is viable to perform both tasks: detection and classification of mitosis. The results show the potential of deep learning in the analysis of Whole Slide Images (WSI) and its integration to computer-aided systems. The extension of this work to whole slide images is also addressed in the last sections; as well as, some computational key points that are useful when constructing a computer-aided-system inspired by the proposed technology.

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Section: Literature Reviewmentioning

confidence: 99%

Deep Learning for Semantic Segmentation vs. Classification in Computational Pathology: Application to Mitosis Analysis in Breast Cancer Grading

Jiménez

Racoceanu

2019

Front. Bioeng. Biotechnol.

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“…The work has been extended in [25]. R. Nateghi et al, (2014) presents an automatic mitosis detection method using cast function and Genetic optimization algorithm. The segmented mitosis and non-mitosis cells were classified using the SVM classifier.…”

Section: Review Based On Conventional Techniquesmentioning

confidence: 99%

“…The segmented mitosis and non-mitosis cells were classified using the SVM classifier. They achieved F-score 78.47% [26]. In [27], statistical detection algorithms, i.e., constrained energy minimization, matched filtering and adaptive coherence estimator has been used for mitosis detection.…”

Section: Review Based On Conventional Techniquesmentioning

confidence: 99%

Efficient deep learning model for mitosis detection using breast histopathology images

Saha

Chakraborty

Racoceanu

2018

Computerized Medical Imaging and Graphics

175

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Mitosis detection is one of the critical factors of cancer prognosis, carrying significant diagnostic information required for breast cancer grading. It provides vital clues to estimate the aggressiveness and the proliferation rate of the tumour. The manual mitosis quantification from whole slide images is a very labor-intensive and challenging task. The aim of this study is to propose a supervised model to detect mitosis signature from breast histopathology WSI images. The model has been designed using deep learning architecture with handcrafted features. We used handcrafted features issued from previous medical challenges MITOS @ ICPR 2012, AMIDA-13 and projects (MICO ANR TecSan) expertise. The deep learning architecture mainly consists of five convolution layers, four max-pooling layers, four rectified linear units (ReLU), and two fully connected layers. ReLU has been used after each convolution layer as an activation function. Dropout layer has been included after first fully connected layer to avoid overfitting. Handcrafted features mainly consist of morphological, textural and intensity features. The proposed architecture has shown to have an improved 92% precision, 88% recall and 90% F-score. Prospectively, the proposed model will be very beneficial in routine exam, providing pathologists with efficient and - as we will prove - effective second opinion for breast cancer grading from whole slide images. Last but not the least, this model could lead junior and senior pathologists, as medical researchers, to a superior understanding and evaluation of breast cancer stage and genesis.

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A survey on automated cancer diagnosis from histopathology images

Jothi

Rajam

2016

Artif Intell Rev

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Automatic detection of mitosis cell in breast cancer histopathology images using genetic algorithm

Cited by 13 publications

References 12 publications

Deep Learning for Semantic Segmentation vs. Classification in Computational Pathology: Application to Mitosis Analysis in Breast Cancer Grading

Deep Learning for Semantic Segmentation vs. Classification in Computational Pathology: Application to Mitosis Analysis in Breast Cancer Grading

Efficient deep learning model for mitosis detection using breast histopathology images

A survey on automated cancer diagnosis from histopathology images

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