Examining the Effect of Color Spaces on Histopathological Image Segmentation with the SHAP Explainable AI Method - Karaaslan, Omer Faruk; Bilgin, Gokhan (IEEE, 2024)
Histopathological images are widely used in the medical field for diagnosis and treatment processes. Despite their rich content in tissue structures, the machine learning algorithms currently employed generally have a black-box structure, limiting their efficient use in segmentation and creating difficulties in examining the obtained results. This study explores SHAP, an explainable artificial intelligence (xAI) method, used for the interpretability of histopathological segmentation processes. With the proposed approach, SHAP value for each color space (RGB, HSV, and L*a*b*) is calculated through the Support Vector Machines (SVM) model, elucidating the impact of these values on segmentation and providing insights into the model. The application of this method suggests its potential to enhance the interpretability of histopathological image segmentation processes using SHAP.
Keywords: Histopathological image analysis; explainable artificial intelligence (xAI) segmentation; interpretability; SHAP; Computer Science; Engineering; Telecommunications