HISTOLOGYPLURALITY votingCANCER diagnosisCELL nucleiDeep learning has been used extensively in histopathological image classification, but people in this field are still exploring new neural network architectures for more effective and efficient cancer diagnosis. Here, we propose multi-scale, multi-view ...
Classification of breast cancer histology images using transfer learning 一、数据集: BACH 2018 grand challenge (ICIAR 2018 Grand Challenge on BreAst Cancer Histology images) iciar2018-challenge.grand-challenge.org 其实算得上Camelyon 16和17 的后继版本。 所不同的是,camelyon 16的数据只有WSI(whole...
In this work, we propose a breast cancer histopathology image classification by assembling multiple compact Convolutional Neural Networks (CNNs). First, a hybrid CNN architecture is designed, which contains a global model branch and a local model branch. By local voting and two-branch information m...
Deep learning has been used extensively in histopathological image classification, but people in this field are still exploring new neural network architectures for more effective and efficient cancer diagnosis. Here, we propose multi-scale, multi-view progressive feature encoding network (MSMV-PFENet) ...
Breast cancer is a common malignancy and a leading cause of cancer-related deaths in women worldwide. Its early diagnosis can significantly reduce the morbidity and mortality rates in women. To this end, histopathological diagnosis is usually followed as
B. “Histopathological image analysis: a review. Biomedical Engineering,” IEEE Reviews in. 2009;2:147-71. [7] F. Spanhol; L. Oliveira; C. Petitjean; L. Heutte, “A Dataset for Breast Cancer Histopathological Image Classification,” in IEEE Transactions on ...
Deep convolutional neural networks for breast cancer histology image analysis. arXiv:1802.00752v2, 2018. Öztürk S, Akdemir B. HIC-net: a deep convolutional neural network model for classification of histopathological breast images. Comput Elect Eng. 2019;76:299–310. Article Google Scholar ...
Microscopic histology image analysis is a cornerstone in early detection of breast cancer. However these images are very large and man- ual analysis is error prone and very time consuming. Thus automating this process is in high demand. We proposed a hierarchical system of con- volutional neural...
cancer grade, er status, histologic subtype, and intrinsic subtype,” NPJ Breast Cancer, vol. 4, no. 1, p. 30, 2018. [3] D. Bardou, K. Zhang, and S. M. Ahmad, “Classification of breast cancer based on histology images using convolutional neural networks,” IEEE ...
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, breas...