Generating diagnostic text from histopathology whole slide images (WSIs) is challenging due to the gigapixel scale of the input and the requirement for precise, domain specific language. We propose a hierarchical vision language framework that combines a frozen pathology foundation model with a Transformer decoder for report generation. To make WSI processing tractable, we perform multi resolution pyramidal patch selection (downsampling factors 2^3 to 2^6) and remove background and artifacts using Laplacian variance and HSV based criteria. Patch features are extracted with the UNI Vision Transformer and projected to a 6 layer Transformer decoder that generates diagnostic text via cross attention. To better represent biomedical terminology, we tokenize the output using BioGPT. Finally, we add a retrieval based verification step that compares generated reports with a reference corpus using Sentence BERT embeddings; if a high similarity match is found, the generated report is replaced with the retrieved ground truth reference to improve reliability.

翻译：从组织病理学全切片图像（WSI）生成诊断文本具有挑战性，原因在于输入图像的千兆像素尺度以及需要精确、领域特定的语言。我们提出了一种分层视觉语言框架，该框架将冻结的病理学基础模型与用于报告生成的Transformer解码器相结合。为使WSI处理可行，我们执行多分辨率金字塔式切片选择（下采样因子为2^3至2^6），并使用基于拉普拉斯方差和HSV的标准去除背景与伪影。切片特征通过UNI Vision Transformer提取，并投影到一个6层Transformer解码器，该解码器通过交叉注意力生成诊断文本。为了更好地表示生物医学术语，我们使用BioGPT对输出进行分词。最后，我们增加了一个基于检索的验证步骤，该步骤使用Sentence BERT嵌入将生成的报告与参考语料库进行比较；若找到高度相似的匹配项，则将生成的报告替换为检索到的真实参考报告，以提高可靠性。