Inference of Captions from Histopathological Patches
Abstract
Computational histopathology has made significant strides in the past few years, slowly getting closer to clinical adoption. One area of benefit would be the automatic generation of diagnostic reports from H&E-stained whole slide images which would further increase the efficiency of the pathologists’ routine diagnostic workflows. In this study, we compiled a dataset (PatchGastricADC22) of histopathological captions of stomach adenocarcinoma endoscopic biopsy specimens, which we extracted from diagnostic reports and paired with patches extracted from the associated whole slide images. The dataset contains a variety of gastric adenocarcinoma subtypes. We trained a baseline attention-based model to predict the captions from features extracted from the patches and obtained promising results. We make the captioned dataset of 262K patches publicly available.
Cite
Text
Tsuneki and Kanavati. "Inference of Captions from Histopathological Patches." Medical Imaging with Deep Learning, 2023.Markdown
[Tsuneki and Kanavati. "Inference of Captions from Histopathological Patches." Medical Imaging with Deep Learning, 2023.](https://mlanthology.org/midl/2023/tsuneki2023midl-inference/)BibTeX
@inproceedings{tsuneki2023midl-inference,
title = {{Inference of Captions from Histopathological Patches}},
author = {Tsuneki, Masayuki and Kanavati, Fahdi},
booktitle = {Medical Imaging with Deep Learning},
year = {2023},
pages = {1235-1250},
volume = {172},
url = {https://mlanthology.org/midl/2023/tsuneki2023midl-inference/}
}