Digital pathology image analysis with deep learning approaches
Observing the stained specimen on the slide with a microscope by pathologists is the foundation of pathological analysis. With the advent and cost-effectiveness of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. This enables the development of computer-aided diagnosis with image or machine learning techniques. Deep learning (DL) is one of the many approaches to machine learning which tries the mimic human brain working using neurons. DL sets new records in accuracy for many important tasks and their related competitions. Since the overwhelming victory of the team using deep learning at ImageNet Large Scale Visual Recognition Competition, most of the image recognition techniques have been replaced by DL. In this project, we focus on using DL techniques to realize the computer-aided diagnosis of histopathology.
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