In this paper, the current approaches and performance needs for whole slide image (WSI) analysis processing in breast cancer diagnostics are discussed. WSIs provide high resolution digital image data from the patient's diseased tissue. Digital whole slide images are typically very large and contain a high amount of information. Digitizing tissue specimen into the form of digital images allows the development and application of computational analysis algorithms. Biological tissues are complex with variance in tissue structures between healthy individuals as well as between patients with the same disease. Furthermore, the tissue preparation and digitization usually generates a lot of artifacts and more complexity, which causes classification challenges. This variance and also the large size of the images make creating an accurate and reliable automated breast cancer image analysis a challenge. In the ALMARVI project we aim at generating and implementing efficient histopathological image analysis algorithms in our breast cancer analysis scheme. This paper focuses on discussing relevant information concerning histopathological breast cancer diagnosis, and could also be considered as an introduction to the concept of WSI analysis to non-experts. Since the WSI sizes are very large (up to 40 GB with no compression) there are challenges on the computational analysis which requires computationally efficient tools and suitable approaches to relieve the problems caused by the large size of the images.
['Irene Pollanen', 'Billy Braithwaite', 'Keijo Haataja', 'Tiia Ikonen', 'Pekka Toivanen']
Current analysis approaches and performance needs for whole slide image processing in breast cancer diagnostics