IASST deploys deep learning network for breast cancer prognosis

A team from the Institute of Advanced Study in Science and Technology (IASST) in Guwahati, has presented the novel deep learning (DL) based quantitative evaluation of oestrogen or progesterone status with the help of Immunohistochemistry (IHC) specimen to grade for prediction of breast cancer. The scientists developed a classification method based on DL network to evaluate hormone status for the prognosis of breast cancer. IHC strain is used as a prognostic marker in breast cancer pathology and involves a special kind of colour staining for identifying malignant nuclei. It possesses different intensity based on which categories are defined in terms of Allred score (ranges 0 to 3) respectively. Scoring systems called Allred and

H-score are used by pathologists in the quantification of the immunohistochemical reaction of oestrogen receptor (ER) and progesterone receptor (PR) tissue slides. Hormone receptors contribute to predicting cancer progression and associated risk of late recurrence of the disease. The team developed an algorithm that indicated whether or not the cancer cells have hormone receptors on their surface. The proposed architecture, namely IHC-Net, can semantically segment the exact positive and negative nuclei from tissue images.