  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.
