التفاصيل البيبلوغرافية
| العنوان: |
Evaluating deep learning-based melanoma classification using immunohistochemistry and routine histology: A three center study. |
| المؤلفون: |
Wies, Christoph, Schneider, Lucas, Haggenmüller, Sarah, Bucher, Tabea-Clara, Hobelsberger, Sarah, Heppt, Markus V., Ferrara, Gerardo, Krieghoff-Henning, Eva I., Brinker, Titus J. |
| المصدر: |
PLoS ONE; 1/19/2024, Vol. 19 Issue 1, p1-13, 13p |
| مصطلحات موضوعية: |
DEEP learning, RECEIVER operating characteristic curves, MELANOMA, HEMATOXYLIN & eosin staining, IMMUNOHISTOCHEMISTRY |
| مستخلص: |
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. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
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