We present ARCH, a computational pathology (CP) multiple instance captioning dataset to facilitate dense supervision of CP tasks. Existing CP datasets focus on narrow tasks; ARCH on the other hand contains dense diagnostic and morphological descriptions for a range of stains, tissue types and pathologies. Using intrinsic dimensionality estimation, we show that ARCH is the only CP dataset to (ARCH-)rival its computer vision analog MS-COCO Captions. We conjecture that an encoder pre-trained on dense image captions learns transferable representations for most CP tasks. We support the conjecture with evidence that ARCH representation transfers to a variety of pathology sub-tasks better than ImageNet features or representations obtained via self-supervised or multi-task learning on pathology images alone. We release our best model and invite other researchers to test it on their CP tasks.

ARCH是一个计算病理学（CP）多实例字幕数据集，包含对一系列染色、组织类型和病理的诊断和形态学描述，我们使用内在维度估计，显示ARCH是唯一可以与计算机视觉类比的MS-COCO Captions匹敌的CP数据集，我们推测在密集的图像字幕上预训练的编码器可以学习可迁移表示，支持这个假设的证据是ARCH表示比ImageNet功能或通过自我监督或多任务学习仅在病理图像上获得的表示更好地转移至各种病理亚任务，我们发布了我们的最佳模型，并邀请其他研究人员在其CP任务上进行测试。

多实例字幕生成：从组织病理学教材和文章中学习表征