generate slides from the attachment l that explains architecture of the model training setup - less than 4 slides - focus on BRAF prediction related content - left paste relevant figure and bullet point explanation on the right. succinct

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This research focuses on the BRAF prediction using the Virchow2 model, which leverages pathology features to enhance accuracy. The study aims to address the critical question of whether to prioritize scale or representation in predictions. The model architecture includes a schematic of the Virchow2 design, emphasizing mixed-magnification patch tokenization and DINOv2-style self-supervised pretraining, which are pivotal for processing BRAF signals effectively.