Abstract
Classification of lung cancer subtypes is a critical clinical step; however, relying solely on H&E-stained histopathology images can pose challenges, and additional immunohistochemical analysis is sometimes required for definitive subtyping. Digital pathology facilitates the use of artificial intelligence for automatic classification of digital tissue slides. Automatic classification of Whole Slide Images (WSIs) typically involves extracting features from patches obtained from them. The aim of this study was to develop a WSI classification framework utilizing an optimizable kernel to encode features from each patch from a WSI into a desirable and adjustable latent space using an evolutionary algorithm. The encoded data can then be used for classification and segmentation while being computationally more efficient. Our proposed framework is compared with a state-of-the-art model, Vim4Path, on an internal and external dataset of lung cancer WSIs. The proposed model outperforms Vim-S16 in accuracy and F1 score at both ×2.5 and ×10 magnification levels on the internal test set, with the highest accuracy (0.833) and F1 score (0.721) at ×2.5. On the external test set, Vim-S16 at ×10 achieves the highest accuracy (0.732), whereas OKEN-DenseNet121 at ×2.5 has the best F1 score (0.772). In future work, finding a dynamic way to tune the output dimensions of the evolutionary algorithm would be of value.