Deep features for breast cancer histopathological image classification

Spanhol, Fábio Alexandre; Oliveira, Luiz S.; Cavalin, Paulo Rodrigo; Petitjean, Caroline; Heutte, Laurent

doi:10.1109/smc.2017.8122889

Cited by 236 publications

(130 citation statements)

References 18 publications

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“…Surprisingly, we achieved 85.1% of accuracy using the TCNN without data augmentation, a performance comparable to the baseline which employs an AlexNet CNN with millions of trainable parameters. Approach Accuracy (%) CNN (Alexnet) [7] 84.6 TCNN (DA 1×) 85.1 Baseline [4] 85.1 TCNN Inc (DA 72×) 85.7 Deep Features (DeCaf) [12] 86.3 CNN+Fisher [13] 86.9 MI Approach [11] 87.2 Inception V3 FT (DA 72×) 87.4…”

Section: Resultsmentioning

confidence: 99%

Texture CNN for Histopathological Image Classification

Matos¹,

Britto

Oliveira

et al. 2019

2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)

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Biopsies are the gold standard for breast cancer diagnosis. This task can be improved by the use of Computer Aided Diagnosis (CAD) systems, reducing the time of diagnosis and reducing the inter and intra-observer variability. The advances in computing have brought this type of system closer to reality. However, datasets of Histopathological Images (HI) from biopsies are quite small and unbalanced what makes difficult to use modern machine learning techniques such as deep learning. In this paper we propose a compact architecture based on texture filters that has fewer parameters than traditional deep models but is able to capture the difference between malignant and benign tissues with relative accuracy. The experimental results on the BreakHis dataset have show that the proposed texture CNN achieves almost 90% of accuracy for classifying benign and malignant tissues.

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Section: Resultsmentioning

confidence: 99%

Texture CNN for Histopathological Image Classification

Matos¹,

Britto

Oliveira

et al. 2019

2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)

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“…Although these methods provide positive contributions to the decision‐making process, they have not been used as a single decision‐making mechanism. Since the proposed systems produce a numerical value indicating the result of the classification for the WSI patches, they are often used to form an advisory system for the pathologist . When examined in this regard, these studies do not contribute to the pathologist's period of decision, they only serve as a quick consultative mechanism for the decision to be made.…”

Section: Cell‐type Based Semantic Segmentationmentioning

confidence: 99%

“…Many algorithms for classifying whole‐slide histopathological images and assistive technology for diagnosis have been presented in literature. These methods can be grouped into traditional methods and advanced methods, or supervised methods and unsupervised methods, or methods based on hand‐crafted features and automatic feature generation . In the early studies in the literature, cells in the images are detected by clustering algorithms such as threshold techniques, fuzzy c‐means .…”

Section: Introductionmentioning

confidence: 99%

“…These methods can be grouped into traditional methods and advanced methods, or supervised methods and unsupervised methods, or methods based on hand-crafted features and automatic feature generation. 5 In the early studies in the literature, cells in the images are detected by clustering algorithms such as threshold techniques, fuzzy c-means. 6 Petushi et al 7 use adaptive threshold and morphological operations for histopathological cell nuclei segmentation.…”

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confidence: 99%

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Cell‐type based semantic segmentation of histopathological images using deep convolutional neural networks

Öztürk

Akdemïr

2019

Int J Imaging Syst Tech

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Histopathological whole‐slide image (WSI) analysis is still one of the most important ways to identify regions of cancer risk. For cancer in which early diagnosis is vital, pathologists are at the center of the decision‐making process. Thanks to the widespread use of digital pathology and the development of artificial intelligence methods, automatic histopathological image analysis methods help pathologists in their decision‐making process. In this process, rather than producing labels for whole‐slide image patches, semantic segmentation is very useful, which facilitates the pathologists’ interpretation. In this study, automatic semantic segmentation based on cell type is proposed for the first time in the literature using novel deep convolutional networks structure (DCNN). We presents semantic information on four classes, including white areas in the whole‐slide image, tissue without cells, tissue with normal cells and tissue with cancerous cells. This visual information presented to the pathologist is an easy‐to‐understand picture of the status of the cells and their implications for the spread of cancerous cells. A new DCNN architecture is created, inspired by the residual network and deconvolution network architecture. Our network is trained end‐to‐end manner with histopathological image patches for cell structures to be more discriminative. The proposed method not only produces more successful results than other state‐of‐art semantic segmentation algorithms with 9.2% training error and 88.89% F‐score for test, but also has the most important advantage in that it has the ability to generate automatic information about the cancer and also provides information that pathologists can quickly interpret.

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“…Then handcrafted features of a query image are passed to the trained algorithm for a yes/no label [4]- [10]. With the success of deep learning, data-driven methods, especially the end-to-end training of convolutional neural network, are adopted more often in recent breast cancer histopathology image classification studies [11]- [13]. Though breast cancer image diagnosis has achieved impressive progress, the issue of self-interpretability in existing diagnosis approaches is less addressed.…”

Section: Introductionmentioning

confidence: 99%

Discriminative Pattern Mining for Breast Cancer Histopathology Image Classification via Fully Convolutional Autoencoder

Radulović

Kanjer

et al. 2019

IEEE Access

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Accurate diagnosis of breast cancer in histopathology images is challenging due to the heterogeneity of cancer cell growth as well as of a variety of benign breast tissue proliferative lesions. In this paper, we propose a practical and selfinterpretable invasive cancer diagnosis solution. With minimum annotation information, the proposed method mines contrast patterns between normal and malignant images in unsupervised manner and generates a probability map of abnormalities to verify its reasoning. Particularly, a fully convolutional autoencoder is used to learn the dominant structural patterns among normal image patches. Patches that do not share the characteristics of this normal population are detected and analyzed by one-class support vector machine and 1-layer neural network. We apply the proposed method to a public breast cancer image set. Our results, in consultation with a senior pathologist, demonstrate that the proposed method outperforms existing methods. The obtained probability map could benefit the pathology practice by providing visualized verification data and potentially leads to a better understanding of data-driven diagnosis solutions.

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Deep features for breast cancer histopathological image classification

Cited by 236 publications

References 18 publications

Texture CNN for Histopathological Image Classification

Texture CNN for Histopathological Image Classification

Cell‐type based semantic segmentation of histopathological images using deep convolutional neural networks

Discriminative Pattern Mining for Breast Cancer Histopathology Image Classification via Fully Convolutional Autoencoder

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