Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model

By Zhongyi Han, Benzheng Wei, Yuanjie Zheng, Yilong Yin, Kejian Li, Sijie Li. Scientific Reports, 2017

623 citations recorded by the source corpus.

Automated breast cancer multi-classification from histopathological images plays a key role in computer-aided breast cancer diagnosis or prognosis. Breast cancer multi-classification is to identify subordinate classes of breast cancer (Ductal carcinoma, Fibroadenoma, Lobular carcinoma, etc.). However, breast cancer multi-classification from histopathological images faces two main challenges from: (1) the great difficulties in breast cancer multi-classification methods contrasting with the classification of binary classes (benign and malignant), and (2) the subtle differences in multiple classes due to the broad variability of high-resolution image appearances, high coherency of cancerous cells, and extensive inhomogeneity of color distribution. Therefore, automated breast cancer multi-classification from histopathological images is of great clinical significance yet has never been explored. Existing works in literature only focus on the binary classification but do not support further breast cancer quantitative assessment. In this study, we propose a breast cancer multi-classification method using a newly proposed deep learning model. The structured deep learning model has achieved remarkable performance (average 93.2% accuracy) on a large-scale dataset, which demonstrates the strength of our method in providing an efficient tool for breast cancer multi-classification in clinical settings.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/5a014925a0ac9e329a77ed5101cc6949745f6344

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

BACH: Grand challenge on breast cancer histology images (2018, background): “Despite the high performance of these systems for the binary classification (healthy vs malignant) of microscopy [5, 6, 7, 8] and wholeslide images [9], the previously referred standard clinical classification procedure has now only started to be explored [10, 11, 12, 13].”
Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges (2019, methodology): “…Bardou et al. 2018; Bayramoglu et al. 2017; Bejnordi et al. 2017b; Cao et al. 2016; Chang et al. 2017; Feng et al. 2018; Gandomkar et al. 2018; Han et al. 2017b; Murtaza et al. 2019; Nahid and Kong 2017b, 2018; Nahid et al. 2018; Nejad et al. 2017; Spanhol et al. 2016a, 2017; Wan et al. 2017;…”
Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network (2018, methodology): “Patient Level LeNet + Aug [18] 2017 48.”
Comparison of Deep Transfer Learning Strategies for Digital Pathology (2018, background): “More specifically, digital pathology and microscopy are not outdone with works on tissue texture [19], cell nuclei [3] and breast cancer [15] classification from histopathology images or analysis of high-content microscopy data [21].”
Deep Learning and Medical Diagnosis: A Review of Literature (2018, neutral): “[32] CSDCNN Mammography Multi-classification of breast cancer; a great performance of 93.”