Scale normalization of histopathological images for batch invariant cancer diagnostic models.
Histopathological images acquired from different experimental set-ups often suffer from batch-effects due to color variations and scale variations. In this paper, we develop a novel scale normalization model for histopathological images based on nuclear area distributions. Results indicate that the normalization model closely fits empirical values for two renal tumor datasets. We study the effect of scale normalization on classification of renal tumor images. Scale normalization improves classification performance in most cases. However, performance decreases in a few cases. In order to understand this, we propose two methods to filter extracted image features that are sensitive to image scaling and features that are uncorrelated with scaling factor. Feature filtering improves the classification performance of cases that were initially negatively affected by scale normalization.
Department of Electrical and Computer Engineering, Georgia Institute of Technology, GA, USA. sk9@gatech.edu
Sonal
S
Kothari
John H
JH
Phan
May D
MD
Wang
D000465:Algorithms; D001706:Biopsy / Q000379:methods* / Q000592:standards; D006801:Humans; D007089:Image Enhancement / Q000379:methods* / Q000592:standards; D007090:Image Interpretation, Computer-Assisted / Q000379:methods* / Q000592:standards; D007680:Kidney Neoplasms / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods* / Q000592:standards; D012016:Reference Values; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity