[论文解读] DeepGleason: a System for Automated Gleason Grading of Prostate Cancer using Deep Neural Networks

Dominik Müller, Philip Meyer+9 more|arXiv (Cornell University)|Mar 25, 2024

AI in cancer detection被引用 5

一句话总结

DeepGleason 提供一个开源的、基于块级的深度学习系统，使用 ConvNeXt 对全切片前列腺癌影像进行自动 Gleason 分级，取得高宏观 F1、AUC 和准确率。

ABSTRACT

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: https://github.com/frankkramer-lab/DeepGleason.

研究动机与目标

• 促进自动 Gleason 分级，以支持数字病理学中的临床决策。
• 开发一个开源、可复用的 AI 工具，用于块级前列腺癌分级。
• 评估用于全切片图像块级分类的最先进架构。
• 在一个大规模、带注释的前列腺切片数据集上展示性能基准。

提出的方法

• 对全切片组织病理图像进行块级图像分类。
• 将微调的图像预处理技术与 ConvNeXt 神经网络相结合。
• 将 ConvNeXt 与包括 transformers 在内的最先进架构进行比较。
• 在自有数据集上进行训练和验证，该数据集包含来自 369 张切片的 34,264 个带注释的块。
• 在 AUCMEDI 框架内实现并进行开源发布。

实验结果

研究问题

• RQ1 一个块级深度学习模型是否能够在全切片前列腺组织病理图像上准确执行 Gleason 分级？
• RQ2 在给定数据集上，哪种神经网络架构能在块级 Gleason 分类中获得更优性能？
• RQ3 所提出的系统是否达到临床相关的良恶性检测和 Gleason 分级判别指标？

主要发现

• 在评估数据集上的宏平均 F1 分数为 0.806。
• AUC 为 0.991。
• 准确率为 0.974。
• ConvNeXt 在本数据集中优于包括 transformers 在内的其他架构。
• 良恶性检测：敏感度 0.94，特异性 0.98。
• Gleason 3 与 Gleason 4 & 5 分类：敏感度 0.91，特异性 0.75。