Evaluating Deep Learning-based Melanoma Classification using Immunohistochemistry and Routine Histology: A Three Center Study
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866914271085985792 |
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| author | Wies, Christoph Schneider, Lucas Haggenmueller, Sarah Bucher, Tabea-Clara Hobelsberger, Sarah Heppt, Markus V. Ferrara, Gerardo Krieghoff-Henning, Eva I. Brinker, Titus J. |
| author_facet | Wies, Christoph Schneider, Lucas Haggenmueller, Sarah Bucher, Tabea-Clara Hobelsberger, Sarah Heppt, Markus V. Ferrara, Gerardo Krieghoff-Henning, Eva I. Brinker, Titus J. |
| contents | Pathologists routinely use immunohistochemical (IHC)-stained tissue slides against MelanA in addition to hematoxylin and eosin (H&E)-stained slides to improve their accuracy in diagnosing melanomas. The use of diagnostic Deep Learning (DL)-based support systems for automated examination of tissue morphology and cellular composition has been well studied in standard H&E-stained tissue slides. In contrast, there are few studies that analyze IHC slides using DL. Therefore, we investigated the separate and joint performance of ResNets trained on MelanA and corresponding H&E-stained slides. The MelanA classifier achieved an area under receiver operating characteristics curve (AUROC) of 0.82 and 0.74 on out of distribution (OOD)-datasets, similar to the H&E-based benchmark classification of 0.81 and 0.75, respectively. A combined classifier using MelanA and H&E achieved AUROCs of 0.85 and 0.81 on the OOD datasets. DL MelanA-based assistance systems show the same performance as the benchmark H&E classification and may be improved by multi stain classification to assist pathologists in their clinical routine. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_03494 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Evaluating Deep Learning-based Melanoma Classification using Immunohistochemistry and Routine Histology: A Three Center Study Wies, Christoph Schneider, Lucas Haggenmueller, Sarah Bucher, Tabea-Clara Hobelsberger, Sarah Heppt, Markus V. Ferrara, Gerardo Krieghoff-Henning, Eva I. Brinker, Titus J. Image and Video Processing Computer Vision and Pattern Recognition Applications Pathologists routinely use immunohistochemical (IHC)-stained tissue slides against MelanA in addition to hematoxylin and eosin (H&E)-stained slides to improve their accuracy in diagnosing melanomas. The use of diagnostic Deep Learning (DL)-based support systems for automated examination of tissue morphology and cellular composition has been well studied in standard H&E-stained tissue slides. In contrast, there are few studies that analyze IHC slides using DL. Therefore, we investigated the separate and joint performance of ResNets trained on MelanA and corresponding H&E-stained slides. The MelanA classifier achieved an area under receiver operating characteristics curve (AUROC) of 0.82 and 0.74 on out of distribution (OOD)-datasets, similar to the H&E-based benchmark classification of 0.81 and 0.75, respectively. A combined classifier using MelanA and H&E achieved AUROCs of 0.85 and 0.81 on the OOD datasets. DL MelanA-based assistance systems show the same performance as the benchmark H&E classification and may be improved by multi stain classification to assist pathologists in their clinical routine. |
| title | Evaluating Deep Learning-based Melanoma Classification using Immunohistochemistry and Routine Histology: A Three Center Study |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Applications |
| url | https://arxiv.org/abs/2309.03494 |