The amount of radiological imaging data being generated is growing at an unprecedented
pace. Concurrently, analytical tools in image processing and big data are making it feasible …

Neurodegenerative disease pathologies have been reported in both grey matter (GM) and
white matter (WM) with different density distributions, an automated separation task of …

This chapter investigates the problem of survival prediction using pathological images. A
novel pipeline is proposed to extract subtype cellular imaging biomarkers for prediction [8] …

Benefiting from the recent progress of AI research, computerized supports in making
decisions have been deployed to a large variety of computer systems supporting decision …

Deep learning-based techniques have been widely employed in solving various medical
image analytical problems. Currently, most of these methods directly employ deep …

In recent years, computational pathology has emerged as a discipline representing big-data
based approaches for the diagnosis and prognosis of cancer patients using different …

With the advance of recent technological innovations, nowadays scientists can easily
capture and store tremendous amounts of different types of medical data such as Computed …

This thesis examines countermeasures to data scarcity of histopathology images using deep
learning techniques. The current literature shows virtually less significant research on …