Digital pathology is an image-based information environment which is enabled by computer technology that allows for the management of information generated from a digital slide. Digital pathology is enabled in part by virtual microscopy, which is the practice of converting glass slides into digital slides that can be viewed, managed, shared and analyzed on a computer monitor. With the advent of Whole-Slide Imaging, the field of digital pathology has exploded and is currently regarded as one of the most promising avenues of diagnostic medicine in order to achieve even better, faster and cheaper diagnosis, prognosis and prediction of cancer and other important diseases.

Breast cancer is the most common cancer among womend and a major cause of death worldwide. According to Nottinghan Grading system there are three important morphological features on Hematoxylin and Eosin (H&E) stained slides for breast cancer grading. They are mitotic coutn, tubule formation, and nuclear pleomorphism. Among them,mitotic count is the most important biomarker.

1. DeepMitosis: Mitosis detection via deep detection, verification and segmentation networks. ( Chao Li et al. 2017 )
2. Mitosis Detection for Invasive Breast Cancer Grading in Histopathological Images.( Paul et al. 2015 )
3. Mitosis Detection in Breast Cancer Histology Images via Deep Cascaded Networks. ( Chen et al. 2016 )
4. Cascaded Ensemble of Convolutional Neural Networks and Handcrafted Features for Mitosis Detection.( Wang et al. 2014 )
5. Mitosis detection in Breast Cancer Histology Images with Deep Neural Networks. ( Ciresan et al. 2013 )
6. Automated mitosis detection in histopahology using morphological and multi-channel statistics features ( Irshad et al. 2013 )
7. An Automatic Mitosis detection method for breast cancer histopathology slide images based on objective and pixel-wise textural features classifcation. ( Tashk et al. 2013 )
8. Classification of mitotic figures with convolutional neural networks and seeded blob features. ( Malon et al. 2013 )

1. ICPR 2012 - http:https://ludo17.free.fr/mitos_2012/download.html
2. MITOS-ATYPIA-14 - https://mitos-atypia-14.grand-challenge.org/
3. Tumor Proliferation Assessment Challenge 2016 - http:https://tupac.tue-image.nl/

1. Automatic Grading of Breast Cancer Whole-Slide Histopathology Images(Thomas Wollmann).
2. Tumor Proliferation Assessment of Whole Slide Images (Martin Rousson).
3. Multi-class single-lable classification of histopathological whole-slide images(Daniel Bug).