A novel ensemble feature selection method for pixel-level segmentation of HER2 overexpression

Abstract

Classifying histopathology images on a pixel-level requires sets of features able to capture the complex characteristics of the images, like the irregular cell morphology and the color heterogeneity on the tissue aspect. In this context, feature selection becomes a crucial step in the classification process such that it reduces model complexity and computational costs, avoids overfitting, and thereby it improves the model performance. In this study, we propose a new ensemble feature selection method by combining a set of base selectors, classifiers, and rank aggregation methods, aiming to determine from any initial set of handcrafted features, a smaller set of relevant color and texture pixel-level features, subsequently used for segmenting HER2 overexpression on a pixel-level, in breast cancer tissue images. We have been able to significantly reduce the set of initial features, using the proposed ensemble feature selection method. The best results are obtained using $$\chi ^2$$ χ 2 , Random Forest, and Runoff as the based selector, classifier, and aggregation method, respectively. The classification performance of the best model trained on the selected features set results in 0.939 recall, 0.866 specificity, 0.903 accuracy, 0.875 precision, and 0.906 F1-score.

HER2 classification in breast cancer cells: A new explainable machine learning application for immunohistochemistry

“…In the present study, the signal was transformed into an inverted binary, where the surrounding blanks by the nuclear signal were converted into particles with a size ranging between 250 and 2,500 pixels2 and medium circularity when working with images with a standardized resolution of 1,000 pixels in width ( 47 ). This strategy allows for the generation of statistically significant differences when comparing amplified vs. normal HER2 expression samples (set of positive vs. negative controls), based on a reduced analytical complexity in comparison with current analytical methods of membrane signals from other research groups that use a skeletonization technique or cell segmentation through the specialized filters ( 48 , 49 ). Moreover, our automated process also used a low complexity method to extract the IHC signal by decomposing the colors into RGB data matrices pixel by pixel, where the combinatorial intensity in red, green, and blue allowed the pixels to be separated.…”

NSGA-II-DL: Metaheuristic Optimal Feature Selection With Deep Learning Framework for HER2 Classification in Breast Cancer

“…The selection of acceptable features for the HER2 image was the subject of fewer investigations since it is difficult due to the complex structure of the HER2 image. Using a binary pixel classification method, [26] showed a new way to pick out representative features in order to separate images of breast cancer that show high levels of HER2. They were able to preserve good classification performance by reducing the original collection of 210 color and texture characteristics to 65 features.…”