Image Descriptors for Weakly Annotated Histopathological Breast Cancer Data - Stanitsas, Panagiotis; Cherian, Anoop; Morellas, Vassilios; Tejpaul, Resha; Papanikolopoulos, Nikolaos; Truskinovsky, Alexander (Frontiers in Digital Health, 2020)
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. We start with a patch level descriptor, termed Covariance-Kernel Descriptor (CKD), capable of compactly describing tissue architectures associated with carcinomas. The CKD captures higher-order correlations between features yet is simple to compute and was shown to achieve superior performance against a large collection of computer vision features on a private breast cancer dataset. To leverage the recognition capability of the CKDs to larger slide regions, we resort to a multiple instance learning framework. In that direction, we derive the Weakly Annotated Image Descriptor (WAID) as the parameters of classifier decision boundaries in a Multiple Instance Learning framework. The WAID is computed on bags of patches corresponding to larger image regions for which binary labels (malignant VS benign) are provided, thus obviating the necessity for tissue delineations. Our experiments on the Breast Cancer Histopathological database (BreakHis) show that the WAID achieves state-of-the-art performance on the database.