Classification of Breast Cancer Histology Images Using Transfer Learning

Ahmad, Hafiz Mughees; Ghuffar, Sajid; Khurshid, Khurram

doi:10.1109/ibcast.2019.8667221

Cited by 52 publications

(28 citation statements)

References 19 publications

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“…Many of our predecessors in digital histopathologic image analysis have used transfer learning techniques, mostly by using weights from CNN architectures pre-trained on large generalized image datasets such as ImageNet [ 13 ], to reduce training time and to benefit from potential performance benefits [ 7 , 14 , 15 , 34 ]. Although there was a significant reduction in training time, the performance results were highly variable, even with the same pre-trained CNN architectures [ 4 ].…”

Section: Discussionmentioning

confidence: 99%

“…Weights previously trained on large-scale datasets such as ImageNet [ 13 ] can be used to initiate training of the model on a different task. Such strategy known as transfer learning have reportedly shown to facilitate faster convergence and better prediction performance for CNNs in digital pathology [ 7 , 14 ]. For example, Nishio et al [ 15 ] have shown that VGG16 [ 16 ] with transfer learning performed better overall than same models trained without transfer learning.…”

Section: Introductionmentioning

confidence: 99%

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Integrative Data Augmentation with U-Net Segmentation Masks Improves Detection of Lymph Node Metastases in Breast Cancer Patients

Jin

Jia

Ashraf

et al. 2020

Cancers

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Deep learning models have potential to improve performance of automated computer-assisted diagnosis tools in digital histopathology and reduce subjectivity. The main objective of this study was to further improve diagnostic potential of convolutional neural networks (CNNs) in detection of lymph node metastasis in breast cancer patients by integrative augmentation of input images with multiple segmentation channels. For this retrospective study, we used the PatchCamelyon dataset, consisting of 327,680 histopathology images of lymph node sections from breast cancer. Images had labels for the presence or absence of metastatic tissue. In addition, we used four separate histopathology datasets with annotations for nucleus, mitosis, tubule, and epithelium to train four instances of U-net. Then our baseline model was trained with and without additional segmentation channels and their performances were compared. Integrated gradient was used to visualize model attribution. The model trained with concatenation/integration of original input plus four additional segmentation channels, which we refer to as ConcatNet, was superior (AUC 0.924) compared to baseline with or without augmentations (AUC 0.854; 0.884). Baseline model trained with one additional segmentation channel showed intermediate performance (AUC 0.870-0.895). ConcatNet had sensitivity of 82.0% and specificity of 87.8%, which was an improvement in performance over the baseline (sensitivity of 74.6%; specificity of 80.4%). Integrated gradients showed that models trained with additional segmentation channels had improved focus on particular areas of the image containing aberrant cells. Augmenting images with additional segmentation channels improved baseline model performance as well as its ability to focus on discrete areas of the image.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integrative Data Augmentation with U-Net Segmentation Masks Improves Detection of Lymph Node Metastases in Breast Cancer Patients

Jin

Jia

Ashraf

et al. 2020

Cancers

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“…In [129], transfer learning based on AlexNet, GoogleNet, and ResNet is used to classify the histopathological images of breast cancer. The result shows that ResNet has the highest accuracy, achieving 83.60% and 85.0% accuracy at the patch level and image level, respectively.…”

Section: ) ''Bioimaging 2015 Breast Histology Classification Challenmentioning

confidence: 99%

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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“…Ahmad et al [31] investigated the effect of transfer learning on a multiclass histopathology dataset, using three CNN architectures, namely the AlexNet, GoogleNet, and ResNet architectures. The dataset used was the BioImaging dataset, and the authors used image augmentation to increase the size of the dataset from 260 images to 72,800 images.…”

Section: Literature Reviewmentioning

confidence: 99%

“…As for other authors who used different histopathology datasets, Ahmad et al [31] reported an accuracy of 85% for the BioImaging dataset using a fine-tuned ResNet architecture, but the authors did not report the transfer learning technique they used to achieve this result. Sharma et al [17] achieved an accuracy of 92.6% and an AUC of 95.65% by using the VGG16 network with logistic regression as a classifier, but the authors did not investigate the role each block plays in the network performance.…”

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confidence: 96%

How Deeply to Fine-Tune a Convolutional Neural Network: A Case Study Using a Histopathology Dataset

Kandel

Castelli

2020

Applied Sciences

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Accurate classification of medical images is of great importance for correct disease diagnosis. The automation of medical image classification is of great necessity because it can provide a second opinion or even a better classification in case of a shortage of experienced medical staff. Convolutional neural networks (CNN) were introduced to improve the image classification domain by eliminating the need to manually select which features to use to classify images. Training CNN from scratch requires very large annotated datasets that are scarce in the medical field. Transfer learning of CNN weights from another large non-medical dataset can help overcome the problem of medical image scarcity. Transfer learning consists of fine-tuning CNN layers to suit the new dataset. The main questions when using transfer learning are how deeply to fine-tune the network and what difference in generalization that will make. In this paper, all of the experiments were done on two histopathology datasets using three state-of-the-art architectures to systematically study the effect of block-wise fine-tuning of CNN. Results show that fine-tuning the entire network is not always the best option; especially for shallow networks, alternatively fine-tuning the top blocks can save both time and computational power and produce more robust classifiers.

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Classification of Breast Cancer Histology Images Using Transfer Learning

Cited by 52 publications

References 19 publications

Integrative Data Augmentation with U-Net Segmentation Masks Improves Detection of Lymph Node Metastases in Breast Cancer Patients

Integrative Data Augmentation with U-Net Segmentation Masks Improves Detection of Lymph Node Metastases in Breast Cancer Patients

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

How Deeply to Fine-Tune a Convolutional Neural Network: A Case Study Using a Histopathology Dataset

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