A cognitive virtual microscopic framework for knowledge-based exploration of large microscopic images in breast cancer histopathology.
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.
Roux
Ludovic
L
Image & Pervasive Access Lab, UMI CNRS, University Joseph Fourier, Grenoble, France. vislr@i2r.a-star.edu.sg
Tutac
Adina
A
Lomenie
Nicolas
N
Balensi
Didier
D
Racoceanu
Daniel
D
Veillard
Antoine
A
Leow
Wee-Kheng
WK
Klossa
Jacques
J
Putti
Thomas C
TC
D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000473:pathology; D003071:Cognition; D003196:Computer Graphics; D003201:Computers; D003952:Diagnostic Imaging / Q000379:methods; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D051188:Knowledge Bases; D008495:Medical Oncology / Q000379:methods; D008853:Microscopy / Q000379:methods*; D011379:Prognosis; D012984:Software; D014584:User-Computer Interface