Classification of Breast Cancer Histology Using Deep Learning

Golatkar, Aditya; Anand, Deepak; Sethi, Amit

doi:10.1007/978-3-319-93000-8_95

Cited by 114 publications

(48 citation statements)

References 9 publications

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“…Nawaz, W., et al [28] 75.73 -Golatkar, A., et al [64] 79 85 Mahbod A., et al [29] -88.5 Roy, K., et al [65] 77.4 90 Wang, Z., et al [42] 87 92 Ferreira, C. A., et al [66] -93 Our model with Experiment 4 (same domain transfer Learning) 90.5 97.4…”

Section: Methods Patch-wise (%) Image-wise (%)mentioning

confidence: 95%

Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model

et al. 2020

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Breast cancer is a significant factor in female mortality. An early cancer diagnosis leads to a reduction in the breast cancer death rate. With the help of a computer-aided diagnosis system, the efficiency increased, and the cost was reduced for the cancer diagnosis. Traditional breast cancer classification techniques are based on handcrafted features techniques, and their performance relies upon the chosen features. They also are very sensitive to different sizes and complex shapes. However, histopathological breast cancer images are very complex in shape. Currently, deep learning models have become an alternative solution for diagnosis, and have overcome the drawbacks of classical classification techniques. Although deep learning has performed well in various tasks of computer vision and pattern recognition, it still has some challenges. One of the main challenges is the lack of training data. To address this challenge and optimize the performance, we have utilized a transfer learning technique which is where the deep learning models train on a task, and then fine-tune the models for another task. We have employed transfer learning in two ways: Training our proposed model first on the same domain dataset, then on the target dataset, and training our model on a different domain dataset, then on the target dataset. We have empirically proven that the same domain transfer learning optimized the performance. Our hybrid model of parallel convolutional layers and residual links is utilized to classify hematoxylin–eosin-stained breast biopsy images into four classes: invasive carcinoma, in-situ carcinoma, benign tumor and normal tissue. To reduce the effect of overfitting, we have augmented the images with different image processing techniques. The proposed model achieved state-of-the-art performance, and it outperformed the latest methods by achieving a patch-wise classification accuracy of 90.5%, and an image-wise classification accuracy of 97.4% on the validation set. Moreover, we have achieved an image-wise classification accuracy of 96.1% on the test set of the microscopy ICIAR-2018 dataset.

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Section: Methods Patch-wise (%) Image-wise (%)mentioning

confidence: 95%

Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model

et al. 2020

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“…In previous work, several research groups carried out image analyses focused on detection of metastatic breast cancer [38][39][40] and mitosis [41][42][43] using highly curated but relatively small datasets from algorithm evaluation challenges [24][25][26][27] 44 proposed a fully convolutional framework for semantic segmentation of histology images via structured crowdsourcing. This was the first work using crowdsourcing in pathology task which involved a total of 25 participants at different expertise levels from medical students to expert pathologists to generate training data for a deep learning algorithm.…”

Section: Discussionmentioning

confidence: 99%

Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor-Infiltrating Lymphocytes in Invasive Breast Cancer

Han

Gupta

Hou

et al. 2020

The American Journal of Pathology

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Quantitative assessment of Tumor-TIL spatial relationships is increasingly important in both basic science and clinical aspects of breast cancer research. We have developed and evaluated convolutional neural network (CNN) analysis pipelines to generate combined maps of cancer regions and tumor infiltrating lymphocytes (TILs) in routine diagnostic breast cancer whole slide tissue images (WSIs). We produce interactive whole slide maps that provide 1) insight about the structural patterns and spatial distribution of lymphocytic infiltrates and 2) facilitate improved quantification of TILs. We evaluated both tumor and TIL analyses using three CNN networks -Resnet-34, VGG16 and Inception v4, and demonstrated that the results compared favorably to those obtained by what believe are the best published methods. We have produced open-source tools and generated a public dataset consisting of tumor/TIL maps for 1,015 TCGA breast cancer images. We also present a customized web-based interface that enables easy visualization and interactive exploration of high-resolution combined Tumor-TIL maps for 1,015 TCGA invasive breast cancer cases that can be downloaded for further downstream analyses. 5/13

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“…In addition, Rakhlin et al [38] employed several traditional CNNs as feature extractors and gradient boosted trees classifier. Golatkar et al [39] extracted patches that are rich in nuclei and used fine-tuned Inception-v3. They achieved 87.2% and 85% accuracy respectively for 4-class classification used 400 H&E stained breast histology images in an extended dataset released for Breast Cancer Histology Challenge (BACH).…”

Section: ) Discussionmentioning

confidence: 99%

Classification of Breast Cancer Histology Images Using Multi-Size and Discriminative Patches Based on Deep Learning

2019

IEEE Access

130

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The diagnosis of breast cancer histology images with hematoxylin and eosin stained is non-trivial, labor-intensive and often leads to a disagreement between pathologists. Computer-assisted diagnosis systems contribute to help pathologists improve diagnostic consistency and efficiency. With the recent advances in deep learning, convolutional neural networks (CNNs) have been successfully used for histology images analysis. The classification of breast cancer histology images into normal, benign, and malignant sub-classes is related to cells' density, variability, and organization along with overall tissue structure and morphology. Based on this, we extract both smaller and larger size patches from histology images, including cell-level and tissue-level features, respectively. However, there are some sampled cell-level patches that do not contain enough information that matches the image tag. Therefore, we propose a patches' screening method based on the clustering algorithm and CNN to select more discriminative patches. The approach proposed in this paper is applied to the 4-class classification of breast cancer histology images and achieves 95% accuracy on the initial test set and 88.89% accuracy on the overall test set. The results are competitive compared to the results of other state-of-the-art methods. INDEX TERMS Breast cancer histology images, multi-size patches, discriminating patches, CNN, image classification.

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Classification of Breast Cancer Histology Using Deep Learning

Cited by 114 publications

References 9 publications

Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model

Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model

Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor-Infiltrating Lymphocytes in Invasive Breast Cancer

Classification of Breast Cancer Histology Images Using Multi-Size and Discriminative Patches Based on Deep Learning

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