Image Descriptors for Weakly Annotated Histopathological Breast Cancer Data [journal]

Journal

Frontiers in Digital Health - December 7, 2020

Authors

Panagiotis Stanitsas (Ph.D. 2018), Anoop Cherian, Vassilios Morellas (research director), Resha Tejpaul (research professional), Nikolaos Papanikolopoulos (professor), Alexander Truskinovsky

Abstract

Cancerous Tissue Recognition (CTR) methodologies are continuously integrating advancements at the forefront of machine learning and computer vision, providing a variety of inference schemes for histopathological data. Histopathological data, in most cases, come in the form of high-resolution images, and thus methodologies operating at the patch level are more computationally attractive. Such methodologies capitalize on pixel level annotations (tissue delineations) from expert pathologists, which are then used to derive labels at the patch level. In this work, we envision a digital connected health system that augments the capabilities of the clinicians by providing powerful feature descriptors that may describe malignant regions.

Link to full paper

Image Descriptors for Weakly Annotated Histopathological Breast Cancer Data

Keywords

computer vision, machine learning