Advances in digital pathology and artificial intelligence (AI) offer
promising opportunities for clinical decision support and enhancing diagnostic
workflows. Previous studies already demonstrated AI's potential for automated
Gleason grading, but lack state-of-the-art methodology and model reusability.
To address this issue, we propose DeepGleason: an open-source deep neural
network based image classification system for automated Gleason grading using
whole-slide histopathology images from prostate tissue sections. Implemented
with the standardized AUCMEDI framework, our tool employs a tile-wise
classification approach utilizing fine-tuned image preprocessing techniques in
combination with a ConvNeXt architecture which was compared to various
state-of-the-art architectures. The neural network model was trained and
validated on an in-house dataset of 34,264 annotated tiles from 369 prostate
carcinoma slides. We demonstrated that DeepGleason is capable of highly
accurate and reliable Gleason grading with a macro-averaged F1-score of 0.806,
AUC of 0.991, and Accuracy of 0.974. The internal architecture comparison
revealed that the ConvNeXt model was superior performance-wise on our dataset
to established and other modern architectures like transformers. Furthermore,
we were able to outperform the current state-of-the-art in tile-wise
fine-classification with a sensitivity and specificity of 0.94 and 0.98 for
benign vs malignant detection as well as of 0.91 and 0.75 for Gleason 3 vs
Gleason 4 & 5 classification, respectively. Our tool contributes to the wider
adoption of AI-based Gleason grading within the research community and paves
the way for broader clinical application of deep learning models in digital
pathology. DeepGleason is open-source and publicly available for research
application in the following Git repository:
this https URL

利用深度神经网络和全幅组织病理图像，我们提出了 DeepGleason：一种用于自动 Gleason 评分的开源深度神经网络图像分类系统，展示了其在准确和可靠的 Gleason 评级方面的较高性能。