Assessing the Performance of Deep Learning for Automated Gleason Grading in Prostate Cancer
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917621724610560 |
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| author | Müller, Dominik Meyer, Philip Rentschler, Lukas Manz, Robin Hieber, Daniel Bäcker, Jonas Cramer, Samantha Wengenmayr, Christoph Märkl, Bruno Huss, Ralf Kramer, Frank Soto-Rey, Iñaki Raffler, Johannes |
| author_facet | Müller, Dominik Meyer, Philip Rentschler, Lukas Manz, Robin Hieber, Daniel Bäcker, Jonas Cramer, Samantha Wengenmayr, Christoph Märkl, Bruno Huss, Ralf Kramer, Frank Soto-Rey, Iñaki Raffler, Johannes |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_16695 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Assessing the Performance of Deep Learning for Automated Gleason Grading in Prostate Cancer Müller, Dominik Meyer, Philip Rentschler, Lukas Manz, Robin Hieber, Daniel Bäcker, Jonas Cramer, Samantha Wengenmayr, Christoph Märkl, Bruno Huss, Ralf Kramer, Frank Soto-Rey, Iñaki Raffler, Johannes Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Tissues and Organs 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. |
| title | Assessing the Performance of Deep Learning for Automated Gleason Grading in Prostate Cancer |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Tissues and Organs |
| url | https://arxiv.org/abs/2403.16695 |