DeepGleason: a System for Automated Gleason Grading of Prostate Cancer using Deep Neural Networks
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913282157182976 |
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| author | Müller, Dominik Meyer, Philip Rentschler, Lukas Manz, Robin Bäcker, Jonas Cramer, Samantha Wengenmayr, Christoph Märkl, Bruno Huss, Ralf Soto-Rey, Iñaki Raffler, Johannes |
| author_facet | Müller, Dominik Meyer, Philip Rentschler, Lukas Manz, Robin Bäcker, Jonas Cramer, Samantha Wengenmayr, Christoph Märkl, Bruno Huss, Ralf Soto-Rey, Iñaki Raffler, Johannes |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_16678 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DeepGleason: a System for Automated Gleason Grading of Prostate Cancer using Deep Neural Networks Müller, Dominik Meyer, Philip Rentschler, Lukas Manz, Robin Bäcker, Jonas Cramer, Samantha Wengenmayr, Christoph Märkl, Bruno Huss, Ralf Soto-Rey, Iñaki Raffler, Johannes Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Tissues and Organs 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. |
| title | DeepGleason: a System for Automated Gleason Grading of Prostate Cancer using Deep Neural Networks |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Tissues and Organs |
| url | https://arxiv.org/abs/2403.16678 |