Using Transfer Learning with Convolutional Neural Networks to Diagnose Breast Cancer from Histopathological Images

Zhi, Weiming; Yueng, Henry Wing Fung; Chen, Zhenghao; Zandavi, Seid Miad; Lu, Zhicheng; Chung, Yuk Ying

doi:10.1007/978-3-319-70093-9_71

Cited by 39 publications

(32 citation statements)

References 13 publications

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“…In deep ANNs, transfer learning strategies are applied more frequently in the classification of breast histopathological images in recent four years. The papers involved in this article are [56], [57], [64], [65], [78], [82], [85], [86], [100], [106], [108], [112], [121], [125], [126], [129], [137], [155]. Transfer learning is a method used to transfer knowledge acquired from one task to resolve another [157].…”

Section: B Analysis Of Deep Ann Methodsmentioning

confidence: 99%

“…As shown in in FIGURE 19, there are two main approaches for applying transfer learning: (1) Fine-tuning the parameters in the pre-training network according to the required tasks (e.g. [57], [65], [100], [121], [125], [129]). (2) Using a pre-trained network as a feature extractor, and then using these features to train a new classifier (e.g.…”

Section: B Analysis Of Deep Ann Methodsmentioning

confidence: 99%

“…Finally, an accuracy around 87% is achieved on 30% testing images. Similarly, in [57], another transfer learning strategy is applied to the same task, and achieves an overall accuracy around 90%.…”

Section: Related Work Of Breakhis In 2017mentioning

confidence: 99%

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

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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Section: B Analysis Of Deep Ann Methodsmentioning

confidence: 99%

Section: B Analysis Of Deep Ann Methodsmentioning

confidence: 99%

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

et al. 2020

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“…In [ 34 ], the same authors proposed another approach based on the constructive deep neural network to address the same task. Some recent studies exploited transfer learning to remedy the data scarcity while addressing this task such as in [ 21 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ]. In [ 35 ], a combination of deep learning, transfer learning, and generative adversarial network is proposed to improve the classification performance.…”

Section: Related Workmentioning

confidence: 99%

Conventional Machine Learning versus Deep Learning for Magnification Dependent Histopathological Breast Cancer Image Classification: A Comparative Study with Visual Explanation

et al. 2021

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Breast cancer is a serious threat to women. Many machine learning-based computer-aided diagnosis (CAD) methods have been proposed for the early diagnosis of breast cancer based on histopathological images. Even though many such classification methods achieved high accuracy, many of them lack the explanation of the classification process. In this paper, we compare the performance of conventional machine learning (CML) against deep learning (DL)-based methods. We also provide a visual interpretation for the task of classifying breast cancer in histopathological images. For CML-based methods, we extract a set of handcrafted features using three feature extractors and fuse them to get image representation that would act as an input to train five classical classifiers. For DL-based methods, we adopt the transfer learning approach to the well-known VGG-19 deep learning architecture, where its pre-trained version on the large scale ImageNet, is block-wise fine-tuned on histopathological images. The evaluation of the proposed methods is carried out on the publicly available BreaKHis dataset for the magnification dependent classification of benign and malignant breast cancer and their eight sub-classes, and a further validation on KIMIA Path960, a magnification-free histopathological dataset with 20 image classes, is also performed. After providing the classification results of CML and DL methods, and to better explain the difference in the classification performance, we visualize the learned features. For the DL-based method, we intuitively visualize the areas of interest of the best fine-tuned deep neural networks using attention maps to explain the decision-making process and improve the clinical interpretability of the proposed models. The visual explanation can inherently improve the pathologist’s trust in automated DL methods as a credible and trustworthy support tool for breast cancer diagnosis. The achieved results show that DL methods outperform CML approaches where we reached an accuracy between 94.05% and 98.13% for the binary classification and between 76.77% and 88.95% for the eight-class classification, while for DL approaches, the accuracies range from 85.65% to 89.32% for the binary classification and from 63.55% to 69.69% for the eight-class classification.

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“…Study [14] found the last convolutional layer in a model provides more important features in comparison to the final fully connected layers. Study [15] introduced a dual-stage fine-tuning that retrains a fully connected layer first and then the network thoroughly. The research [16] showed that fine-tuning on the last three layers of pre-trained AlexNet network works better than the Support Vector Machine (SVM) classification of concatenated features extracted from two pre-trained networks.…”

Section: Binary Classificationmentioning

confidence: 99%

Deep Learning Applied for Histological Diagnosis of Breast Cancer

Yari

Nguyen

2020

IEEE Access

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Deep learning, as one of the currently most popular computer science research trends, improves neural networks, which has more and deeper layers allowing higher abstraction levels and more accurate data-based. Although deep convolutional neural networks, as a deep learning algorithm, has recently achieved promising results in data analysis, the requirement for a large amount of data prevents its use in medical data analysis since it is challenging to obtain data from the medical field. Breast cancer is a common cancer in women. To diagnose this kind of cancer, breast cell shapes in histopathology images should be examined by senior pathologists. The number of pathologists per population in the world is not enough, especially in Africa, and human mistake may occur in diagnosis procedure. After the evaluation of deep learning methods and algorithms in breast histological data processing, we tried to improve the current systems' accuracy. As a result, this study proposes two effective deep transfer learning-based models, which rely on pre-trained DCNN using a large collection of ImageNet dataset images that improve current state-of-the-art systems in both binary and multiclass classification. We transfer pre-trained weights of the ResNet50 and DesneNet121 on the Imagenet as initial weights and fine-tune these models with a deep classifier with data augmentation to detect various malignant and benign samples tissues in the two categories of binary classification and multiclass classification. The proposed models have been examined with optimized hyperparameters in magnification-dependent and magnification-independent classification modes. In the multiclass classification, the proposed system achieved up to 98% accuracy. As for binary classification, the proposed system provides up to 100% accuracy. The results outperform previous studies accuracies in all defined performance metrics in breast cancer CAD systems from histological images.

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Using Transfer Learning with Convolutional Neural Networks to Diagnose Breast Cancer from Histopathological Images

Cited by 39 publications

References 13 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

Conventional Machine Learning versus Deep Learning for Magnification Dependent Histopathological Breast Cancer Image Classification: A Comparative Study with Visual Explanation

Deep Learning Applied for Histological Diagnosis of Breast Cancer

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