MDC-Net: A New Convolutional Neural Network for Nucleus Segmentation in Histopathology Images with Distance Maps and Contour Information

Abstract Malignancy is one of the leading causes of death globally. It is on the rise in the developed and low-income countries with survival rates of less than 40%. However, early diagnosis may increase survival chances. Histopathology images acquired from the biopsy are a popular method for cancer diagnosis. In this article, we propose a deep convolutional neural network-based method that helps classify breast cancer tumor subtypes from histopathology images. The model is trained on the BreakHis dataset but is also tested on images from other datasets. The model is trained to recognized eight different tumor subtypes, and also to perform binary classification (malignant / non-malignant). The CNN model uses an encoder-decoder architecture as well as a parallel feed-forward network. The proposed model provides higher cumulative training accuracy and statistical scoring after five-fold cross-validation. Comparing with the other models, the accuracy of the proposed model is higher at different magnification and patient levels.

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Automated Classification of Oral Cancer Histopathology images using Convolutional Neural Network

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) ◽

10.1109/bibm47256.2019.8982979 ◽

2019 ◽

Author(s):

Santisudha Panigrahi ◽

Tripti Swarnkar

Keyword(s):

Neural Network ◽

Oral Cancer ◽

Convolutional Neural Network ◽

Automated Classification ◽

Histopathology Images

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Metastatic Breast Cancer Recognition in Histopathology Images Using Convolutional Neural Network with Attention Mechanism

2019 Chinese Automation Congress (CAC) ◽

10.1109/cac48633.2019.8997460 ◽

2019 ◽

Author(s):

Yu Liang ◽

Jinglong Yang ◽

Xiongwen Quan ◽

Han Zhang

Keyword(s):

Breast Cancer ◽

Neural Network ◽

Metastatic Breast Cancer ◽

Convolutional Neural Network ◽

Metastatic Breast ◽

Attention Mechanism ◽

Histopathology Images

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Hybrid Deep Transfer Network and Rotational Sample Subspace Ensemble Learning for Early Cancer Detection

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2020.31722289 ◽

2020 ◽

Vol 10 (10) ◽

pp. 2289-2296

Author(s):

Pin Wang ◽

Shanshan Lv ◽

Yongming Li ◽

Qi Song ◽

Linyu Li ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Image Classification ◽

Ensemble Learning ◽

Cancer Detection ◽

Early Cancer ◽

Early Cancer Detection ◽

Mice Model ◽

Early Cancer Diagnosis ◽

Histopathology Images

Accurate histopathology cell image classification plays an important role in early cancer detection and diagnosis. Currently, Convolutional Neural Network is used to assist pathologists for histopathology image classification. In the paper, a Min mice model was applied to evaluate the capability of Convolutional Neural Network features for detecting early-stage carcinogenesis. However, due to the limited histopathology images of the mice model, it may cause overfitting for the classification. Hence, hybrid deep transfer network and rotational sample subspace ensemble learning is proposed for the histopathology image classification. First, deep features are obtained by deep transfer network based on regularized loss functions. Then, the rotational sample subspace sampling is applied to increase the diversity between training sets. Subsequently, subspace projection learning is introduced to achieve dimensionality reduction. Finally, the ensemble learning is used for histopathology image classification. The proposed method was tested on 126 histopathology images of the mouse model. The experimental results demonstrate that the proposed method has achieved a remarkable classification accuracy (99.39%, 99.74%, 100%). It has demonstrated that the proposed approach is promising for early cancer diagnosis.

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