ABSTRACT
Histopathological examination is a powerful method for prognosis of major diseases such as breast cancer. Analysis of medical images largely remains the work of human experts. Current virtual microscope systems are mainly an emulation of real microscopes with annotation and some image analysis capabilities. However, the lack of effective knowledge management prevents such systems from being computer-aided prognosis platforms. The cognitive virtual microscopic framework, through an extended modeling and use of medical knowledge, has the capacity to analyse histopathological images and to perform grading of breast cancer, providing pathologists with a robust and traceable second opinion.
Subject(s)
Breast Neoplasms/diagnosis , Microscopy/methods , Algorithms , Breast Neoplasms/pathology , Cognition , Computer Graphics , Computers , Diagnostic Imaging/methods , Female , Humans , Image Processing, Computer-Assisted/methods , Knowledge Bases , Medical Oncology/methods , Prognosis , Software , User-Computer InterfaceABSTRACT
Breast cancer grading of histopathological images is the standard clinical practice for the diagnosis and prognosis of breast cancer development. In a large hospital, a pathologist typically handles 100 grading cases per day, each consisting of about 2000 image frames. It is, therefore, a very tedious and time-consuming task. This paper proposes a method for automatic computer grading to assist pathologists by providing second opinions and reducing their workload. It combines the three criteria in the Nottingham scoring system using a multi-resolution approach. To our best knowledge, there is no existing work that provide complete grading according to the Nottingham criteria.
