Improved Anomaly Detection in Low-Resolution and Noisy Whole-Slide Images using Transfer Learning

Whole-slide imaging (WSI) is one of the most recent technologies introduced in medical pathology practices. WSI images are created using a computerized system that scans, stitches and stores pathology specimen glass slides into digital images, which provide a multi-resolution pyramid construction of a huge gigabyte size due to the need for containing a high amount of tissue details. Therefore, digital WSI brings major challenges in data storage, image analysis and transmission (e.g. telepathology and interoperability). In this paper, we propose a computer-aided diagnosis (CAD) system to detect cancer anomalies in breast lymph node WSI images under low-resolution (LR) and noise conditions. In particular, we investigate a transfer-learning approach to find the scale mappings between WSI levels using partial least-square (PLS) regression. The learned scale mappings can be used to detect anomalies in LR images and hence reduce the computational cost of anomaly detection. Then, we explore the effect of different levels of noise on detection performance. We simulated different scenarios where WSI images are contaminated with Gaussian noise and several de-noising algorithms were applied, namely de-noising with PLS, Block Matching $3D (BM3D)$ and the combination of PLS and BM3D. We show that these de-noising algorithms can help reduce the noise severity on anomaly detection. For example, for noisy images with 0.8 noise standard deviation, these three algorithms improved the LR detection accuracy from 63.50% to 93.81%, 92.73%, and 97.51%, respectively. Our results lead to useful conclusions on how to handle whole slide images under scaling and noise conditions.