Deep learning algorithms out-perform veterinary pathologists in detecting the mitotically most active tumor region
| Autor: | Aubreville, Marc, Bertram, Christof A., Marzahl, Christian, Gurtner, Corinne, Dettwiler, Martina, Schmidt, Anja, Bartenschlager, Florian, Merz, Sophie, Fragoso, Marco, Kershaw, Olivia, Klopfleisch, Robert, Maier, Andreas |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Mastocytosis Cutaneous Computer Vision and Pattern Recognition (cs.CV) Tumour heterogeneity Computer Science - Computer Vision and Pattern Recognition lcsh:Medicine Mitosis Machine Learning (stat.ML) Article Machine Learning (cs.LG) Deep Learning Dogs Image processing Statistics - Machine Learning Machine learning Image Processing Computer-Assisted Skin cancer Animals Mast Cells lcsh:Science lcsh:R Translational research Pathologists ddc:000 lcsh:Q Cancer imaging Neoplasm Grading Algorithms |
| Zdroj: | Scientific Reports Scientific Reports, Vol 10, Iss 1, Pp 1-11 (2020) |
| ISSN: | 2045-2322 |
| Popis: | Manual count of mitotic figures, which is determined in the tumor region with the highest mitotic activity, is a key parameter of most tumor grading schemes. It can be, however, strongly dependent on the area selection due to uneven mitotic figure distribution in the tumor section.We aimed to assess the question, how significantly the area selection could impact the mitotic count, which has a known high inter-rater disagreement. On a data set of 32 whole slide images of H&E-stained canine cutaneous mast cell tumor, fully annotated for mitotic figures, we asked eight veterinary pathologists (five board-certified, three in training) to select a field of interest for the mitotic count. To assess the potential difference on the mitotic count, we compared the mitotic count of the selected regions to the overall distribution on the slide.Additionally, we evaluated three deep learning-based methods for the assessment of highest mitotic density: In one approach, the model would directly try to predict the mitotic count for the presented image patches as a regression task. The second method aims at deriving a segmentation mask for mitotic figures, which is then used to obtain a mitotic density. Finally, we evaluated a two-stage object-detection pipeline based on state-of-the-art architectures to identify individual mitotic figures. We found that the predictions by all models were, on average, better than those of the experts. The two-stage object detector performed best and outperformed most of the human pathologists on the majority of tumor cases. The correlation between the predicted and the ground truth mitotic count was also best for this approach (0.963 to 0.979). Further, we found considerable differences in position selection between pathologists, which could partially explain the high variance that has been reported for the manual mitotic count. 13 pages, 7 figures |
| Databáze: | OpenAIRE |
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