Using Transfer Learning to Detect Breast Cancer without Network Training

Pang, Hao; Lin, Wenjie; Zhao, Chen

doi:10.1109/ccis.2018.8691355

“…In [93], Multiple Magnification Feature Embedding (MMFE) is introduced, which is an approach using transfer learning to detect breast cancer from digital pathology images without network training. The main idea of the MMFE method is to simulate the daily diagnosis process of a medical doctor.…”

Section: Related Work Of Camelyon 2016mentioning

confidence: 99%

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

Xiao-min

¹

,

Li

²

,

Rahaman

³

et al. 2020

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Breast cancer is one of the most common and deadliest cancers among women. Since histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of breast cancers. To improve the accuracy and objectivity of Breast Histopathological Image Analysis (BHIA), Artificial Neural Network (ANN) approaches are widely used in the segmentation and classification tasks of breast histopathological images. In this review, we present a comprehensive overview of the BHIA techniques based on ANNs. First of all, we categorize the BHIA systems into classical and deep neural networks for in-depth investigation. Then, the relevant studies based on BHIA systems are presented. After that, we analyze the existing models to discover the most suitable algorithms. Finally, publicly accessible datasets, along with their download links, are provided for the convenience of future researchers.

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“…Jaiswal et al [ 43 ] proposed a single-cycle learning rate policy with two steps throughout the training where LR increases in one step and decreases in the next iteration with a maximum learning rate of 0.00055 and a minimum of 0.0001. The method suggested by Pang et al [ 44 ] takes input image slides of different resolutions scaled to256 × 256 on a pretrained model and reported 78.1% accuracy on embedding tile-based features. Fan et al [ 45 ] generated a heat map using a pretrained model which is trained from patches cropped from whole slide images.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Learning Rate in Deep CNN Model for Metastasis Detection and Classification of Histopathology Images

Johny

¹

2021

Computational and Mathematical Methods in Medicine

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Diagnosis of different breast cancer stages using histopathology whole slide images (WSI) is the gold standard in determining the grade of tissue metastasis. Computer-aided diagnosis (CAD) assists medical experts as a second opinion tool in early detection to prevent further proliferation. The field of pathology has advanced so rapidly that it is possible to obtain high-quality images from glass slides. Patches from the region of interest in histopathology images are extracted and trained using artificial neural network models. The trained model primarily analyzes and predicts the histology images for the benign or malignant class to which it belongs. Classification of medical images focuses on the training of models with layers of abstraction to distinguish between these two classes with less false-positive rates. The learning rate is the crucial hyperparameter used during the training of deep convolutional neural networks (DCNN) to improve model accuracy. This work emphasizes the relevance of the dynamic learning rate than the fixed learning rate during the training of networks. The dynamic learning rate varies with preset conditions between the lower and upper boundaries and repeats at different iterations. The performance of the model thus improves and attains comparatively high accuracy with fewer iterations.

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“…In [94], Multiple Magnification Feature Embedding (MMFE) is introduced, which is an approach using transfer learning to detect breast cancer from digital pathology images without network training. The main idea of the MMFE method is to simulate the daily diagnosis process of a medical doctor.…”

Section: Related Work Of Breakhis In 2020mentioning

confidence: 99%

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

Zhou,

Li,

Rahaman

et al. 2020

Preprint

0

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Breast cancer is one of the most common and deadliest cancers among women. Since histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of breast cancers. To improve the accuracy and objectivity of Breast Histopathological Image Analysis (BHIA), Artificial Neural Network (ANN) approaches are widely used in the segmentation and classification tasks of breast histopathological images. In this review, we present a comprehensive overview of the BHIA techniques based on ANNs. First of all, we categorize the BHIA systems into classical and deep neural networks for in-depth investigation. Then, the relevant studies based on BHIA systems are presented. After that, we analyze the existing models to discover the most suitable algorithms. Finally, publicly accessible datasets, along with their download links, are provided for the convenience of future researchers.

show abstract

Using Transfer Learning to Detect Breast Cancer without Network Training

Cited by 7 publications

References 4 publications

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

Dynamic Learning Rate in Deep CNN Model for Metastasis Detection and Classification of Histopathology Images

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

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