Back
Konstantinos N. Plataniotis (University of Toronto)
Author/s
About the resource/s
Due to the proliferation of whole-slide-imaging (WSI) digital scanners it is now possible to leverage computer vision, image analysis, and machine learning techniques, such as deep learning to process the digital pathology images in hopes to derive, diagnosis and prognosis markers. The convergence of digital imaging, data science and pathology gave rise to a new research area known as digital pathology. Digital pathology greatly enhances diagnostic accuracy and allows a variety of pathology tasks to be completed with greater efficiency. This presentation will offer a general introduction to the topic, it will outline and discuss imaging tasks needed for the successful implementation of a digital pathology pipeline, and it will offer overview and insights on how data-driven solutions such as deep neural networks can be used to derive markers from digital pathology slides. It will also be shown that a diagnostic system that combines deep learning and prior histological knowledge can provide useful diagnostic/prognostic markers. Lastly, open research issues and implementation challenges will be briefly discussed.
Documents
Other Sources
