Few algorithms for histopathological images in computational pathology

Abstract

In computational pathology huge stacks of histological data are made available through advances in the microscopic image acquisition Clinicians cannot learn easily from this big data due to obvious human limitations However machine learning systems can learn important patterns from these huge histological data to improve the diagnosis and prognosis Such automated prediction can act as a second opinion for the experts to help make their decisions In this thesis we address four different pr