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

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…

• NN
  National Natural Science Foundation of China

  Awards: ZR2014FM001, 61572300, ZR2015FM010, 2016WS0577, TSHW201502038, U1201258, J15LN20
• NS
  Natural Science Foundation of Shandong Province

  Awards: J15LN20, 2016WS0577, 61572300, ZR2015FM010, TSHW201502038, ZR2014FM001
• PO
  Project of Shandong Province Higher Educational Science and Technology Program