Prostate cancer is a dominant health concern calling for advanced diagnostic
tools. Utilizing digital pathology and artificial intelligence, this study
explores the potential of 11 deep neural network architectures for automated
Gleason grading in prostate carcinoma focusing on comparing traditional and
recent architectures. A standardized image classification pipeline, based on
the AUCMEDI framework, facilitated robust evaluation using an in-house dataset
consisting of 34,264 annotated tissue tiles. The results indicated varying
sensitivity across architectures, with ConvNeXt demonstrating the strongest
performance. Notably, newer architectures achieved superior performance, even
though with challenges in differentiating closely related Gleason grades. The
ConvNeXt model was capable of learning a balance between complexity and
generalizability. Overall, this study lays the groundwork for enhanced Gleason
grading systems, potentially improving diagnostic efficiency for prostate
cancer.

通过利用数字病理学和人工智能，该研究探索了 11 种深度神经网络架构在自动化前列腺癌格里森分级中的潜力，重点比较了传统和最新的架构。使用基于 AUCMEDI 框架的标准化图像分类流程，在具有 34,264 个注释组织切片的内部数据集上进行了强大的评估。结果显示不同架构的敏感性各不相同，其中 ConvNeXt 表现最好。值得注意的是，即使在区分紧密相关的格里森分级方面存在挑战，新的架构也取得了优越的性能。ConvNeXt 模型能够在复杂性和泛化能力之间学到平衡。总的来说，该研究为改善前列腺癌诊断效率奠定了基础。

评估深度学习在前列腺癌自动格里森分级中的性能

Assessing the Performance of Deep Learning for Automated Gleason Grading  in Prostate Cancer

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。

DeepGleason: 使用深度神经网络进行前列腺癌 Gleason 分级的自动化系统

DeepGleason: a System for Automated Gleason Grading of Prostate Cancer  using Deep Neural Networks

Automatic and accurate Gleason grading of histopathology tissue slides is
crucial for prostate cancer diagnosis, treatment, and prognosis. Usually,
histopathology tissue slides from different institutions show heterogeneous
appearances because of different tissue preparation and staining procedures,
thus the predictable model learned from one domain may not be applicable to a
new domain directly. Here we propose to adopt unsupervised domain adaptation to
transfer the discriminative knowledge obtained from the source domain to the
target domain without requiring labeling of images at the target domain. The
adaptation is achieved through adversarial training to find an invariant
feature space along with the proposed Siamese architecture on the target domain
to add a regularization that is appropriate for the whole-slide images. We
validate the method on two prostate cancer datasets and obtain significant
classification improvement of Gleason scores as compared with the baseline
models.

该文提出采用无监督领域适应方法，利用对抗性训练在整张组织切片图像上实现领域间知识的转移，提高了前列腺癌 Gleason 分数的分类表现。