The field of computational pathology is rapidly advancing, with a focus on developing robust and generalizable methods for image analysis and classification. Recent research has highlighted the importance of incorporating domain-invariant features and morphology-guided approaches to improve the accuracy and reliability of computational pathology models. Additionally, there is a growing interest in developing unified foundation models that can enhance image quality and facilitate image translation tasks. Noteworthy papers in this area include: MorphGen, which proposes a morphology-guided representation learning approach for robust single-domain generalization in histopathological cancer classification. PRINTER, which introduces a deformation-aware adversarial learning framework for virtual IHC staining with in situ fidelity. A Unified Low-level Foundation Model, which presents a single adaptable architecture for enhancing image quality in restoration tasks and facilitating image translation tasks. Latent Gene Diffusion, which addresses the limitations of current models for spatial transcriptomics completion by introducing a reference-free latent gene diffusion model. STAR, which provides a fast and robust rigid registration framework for serial histopathological images. Teacher-Student Model, which formulates mitosis detection as a pixel-level segmentation and proposes a teacher-student model for detecting and classifying mitosis. CARDIE, which introduces an unsupervised algorithm for clustering images based on their color and luminosity content for image enhancement.