In Recent Years, Digital Technologies Have Made Significant Strides In Augmenting-Human-Health, Cognition, And Perception, Particularly Within The Field Of Computational-Pathology. This Paper Presents A Novel Approach To Enhancing The Analysis Of Histopathology Images By Leveraging A Mult-modal-Model That Combines Vision Transformers (Vit) With Gpt-2 For Image Captioning. The Model Is Fine-Tuned On The Specialized Arch-Dataset, Which Includes Dense Image Captions Derived From Clinical And Academic Resources, To Capture The Complexities Of Pathology Images Such As Tissue Morphologies, Staining Variations, And Pathological Conditions. By Generating Accurate, Contextually Captions, The Model Augments The Cognitive Capabilities Of Healthcare Professionals, Enabling More Efficient Disease Classification, Segmentation, And Detection. The Model Enhances The Perception Of Subtle Pathological Features In Images That Might Otherwise Go Unnoticed, Thereby Improving Diagnostic Accuracy. Our Approach Demonstrates The Potential For Digital Technologies To Augment Human Cognitive Abilities In Medical Image Analysis, Providing Steps Toward More Personalized And Accurate Healthcare Outcomes.

翻译：近年来，数字技术在增强人类健康、认知与感知方面取得了显著进展，尤其在计算病理学领域。本文提出一种创新方法，通过利用结合视觉Transformer（ViT）与GPT-2进行图像描述的多模态模型，以增强组织病理学图像分析。该模型在专业ARCH数据集上进行微调，该数据集包含源自临床与学术资源的密集图像描述，旨在捕捉病理图像的复杂性，如组织形态、染色变异及病理状况。通过生成准确且符合语境的描述，该模型增强了医疗专业人员的认知能力，实现了更高效的疾病分类、分割与检测。该模型提升了对图像中可能被忽略的细微病理特征的感知能力，从而提高了诊断准确性。我们的方法展示了数字技术在增强医学图像分析中人类认知能力的潜力，为更个性化、更精准的医疗成果提供了前进方向。