Histopathological Image Recognition with Domain Knowledge Based Deep Features

Zhang, Gang; Xiao, Ming; Huang, Yonghui

doi:10.1007/978-3-319-95957-3_38

Cited by 7 publications

(2 citation statements)

References 16 publications

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“…Most of the previously published breast cancer classification methods based on BreaKHis ( Cascianelli et al, 2017 ; Gupta and Bhavsar, 2017 , 2018 ; Han et al, 2017 ; Song et al, 2017 ; Wei et al, 2017 ; Bardou et al, 2018 ; Benhammou et al, 2018 ; Gandomkar et al, 2018 ; Karthiga and Narasimhan, 2018 ; Li et al, 2018 ; Zhang et al, 2018 ) use binary classification and not fine-grained classification. Furthermore, most binary classification and all fine-grained classification approaches are magnification-specific.…”

Section: Resultsmentioning

confidence: 99%

Fine-Grained Breast Cancer Classification With Bilinear Convolutional Neural Networks (BCNNs)

2020

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Classification of histopathological images of cancer is challenging even for well-trained professionals, due to the fine-grained variability of the disease. Deep Convolutional Neural Networks (CNNs) showed great potential for classification of a number of the highly variable fine-grained objects. In this study, we introduce a Bilinear Convolutional Neural Networks (BCNNs) based deep learning method for fine-grained classification of breast cancer histopathological images. We evaluated our model by comparison with several deep learning algorithms for fine-grained classification. We used bilinear pooling to aggregate a large number of orderless features without taking into consideration the disease location. The experimental results on BreaKHis, a publicly available breast cancer dataset, showed that our method is highly accurate with 99.24% and 95.95% accuracy in binary and in fine-grained classification, respectively.

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Section: Resultsmentioning

confidence: 99%

Fine-Grained Breast Cancer Classification With Bilinear Convolutional Neural Networks (BCNNs)

2020

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“…Study [17] introduced a model called deep domain knowledge-based features that mitigates the gap between the extracted features and the required specific domain that comes from using a pre-trained network on other datasets. The latter study retrained the pre-trained CNN on the BreakHis dataset for efficient feature extraction.…”

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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Fusing of Deep Learning, Transfer Learning and GAN for Breast Cancer Histopathological Image Classification

Thuy

Hoang

2019

Advances in Intelligent Systems and Computing

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Histopathological Image Recognition with Domain Knowledge Based Deep Features

Cited by 7 publications

References 16 publications

Fine-Grained Breast Cancer Classification With Bilinear Convolutional Neural Networks (BCNNs)

Fine-Grained Breast Cancer Classification With Bilinear Convolutional Neural Networks (BCNNs)

Deep Learning Applied for Histological Diagnosis of Breast Cancer

Fusing of Deep Learning, Transfer Learning and GAN for Breast Cancer Histopathological Image Classification

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