Representation learning-based unsupervised domain adaptation for classification of breast cancer histopathology images

Publication date: 2018Source: Biocybernetics and Biomedical Engineering, Volume 38, Issue 3Author(s): Pendar Alirezazadeh, Behzad Hejrati, Alireza Monsef-Esfahani, Abdolhossein FathiAbstractBreast cancer has high incidence rate compared to the other cancers among women. This disease leads to die if it does not diagnosis early. Fortunately, by means of modern imaging procedure such as MRI, mammography, thermography, etc., and computer systems, it is possible to diagnose all kind of breast cancers in a short time. One type of BC images is histology images. They are obtained from the entire cut-off texture by use of digital cameras and contain invaluable information to diagnose malignant and benign lesions. Recently by requesting to use the digital workflow in surgical pathology, the diagnosis based on whole slide microscopy image analysis has attracted the attention of many researchers in medical image processing. Computer aided diagnosis (CAD) systems are developed to help pathologist make a better decision. There are some weaknesses in histology images based CAD systems in compared with radiology images based CAD systems. As these images are collected in different laboratory stages and from different samples, they have different distributions leading to mismatch of training (source) domain and test (target) domain. On the other hand, there is the great similarity between images of benign tumors with those of malignant. So if these images are analyzed undiscriminating, this le...
Source: Biocybernetics and Biomedical Engineering - Category: Biomedical Engineering Source Type: research