The field of computational pathology is witnessing significant advancements, driven by the development of innovative models and techniques. A key direction in this field is the improvement of end-to-end learning methods, which aim to optimize the performance of deep learning models for tasks such as cancer diagnosis and prognosis. Researchers are also exploring the use of foundation models, which have shown great promise in image analysis and can be adapted to various tasks in computational pathology. Noteworthy papers in this area include: Revisiting End-to-End Learning with Slide-level Supervision in Computational Pathology, which proposes a novel MIL called ABMILX to mitigate optimization challenges. A Foundation Model for Spatial Proteomics, which introduces KRONOS, a foundation model built for spatial proteomics that achieves state-of-the-art performance across multiple tasks. MS-YOLO, a multi-scale model for accurate and efficient blood cell detection, which improves detection performance through architectural innovations. Single GPU Task Adaptation of Pathology Foundation Models for Whole Slide Image Analysis, which proposes a novel approach for single-GPU task adaptation of pathology foundation models that maintains separate computational graphs for MIL aggregator and the PFM.