  Whole slide images are the foundation of digital pathology for the diagnosis
and treatment of carcinomas. Writing pathology reports is laborious and
error-prone for inexperienced pathologists. To reduce the workload and improve
clinical automation, we investigate how to generate pathology reports given
whole slide images. On the data end, we curated the largest WSI-text dataset
(PathText). In specific, we collected nearly 10000 high-quality WSI-text pairs
for visual-language models by recognizing and cleaning pathology reports which
narrate diagnostic slides in TCGA. On the model end, we propose the multiple
instance generative model (MI-Gen) which can produce pathology reports for
gigapixel WSIs. We benchmark our model on the largest subset of TCGA-PathoText.
Experimental results show our model can generate pathology reports which
contain multiple clinical clues and achieve competitive performance on certain
slide-level tasks. We observe that simple semantic extraction from the
pathology reports can achieve the best performance (0.838 of F1 score) on BRCA
subtyping surpassing previous state-of-the-art approaches. Our collected
dataset and related code are available.
