Statistical Local Binary Patterns (SLBP): Application To Prostate Cancer Gleason Score Prediction From whole Slide Pathology Images
Year: 2019
Type: conference-paper
Abstract: Computerized whole slide image analysis is important for assisting pathologists in cancer grading and predicting patient clinical outcomes. However, it is challenging to analyze whole slide image (WSI) at cellular level due to its huge size and nuclear variations. For efficient WSI analysis, this paper presents a general texture descriptor, statistical local binary patterns (SLBP), which is applied to prostate cancer Gleason score prediction from WSI. Unlike traditional local binary patterns (LBP) and many its variants, the presented SLBP encodes local texture patterns via analyzing both median and standard deviation over a regional sampling scheme, so that it can capture more microand macro-structure information in the image. Experiments on Gleason score prediction have been performed on 317 different patient cases selected from the cancer genome atlas (TCGA) dataset. The presented SLBP descriptor provides over 80% accuracy on two-class (grade ≤7 vs grade ≥8) distinction, which is superior to traditional texture descriptors such as histogram, Haralick and other state-of-the-art LBP variants.
Authors: Hongming Xu, Tae Hyun Hwang
Institution: Cleveland Clinic
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2958110063