تقرير
Evaluating Deep Learning-based Melanoma Classification using Immunohistochemistry and Routine Histology: A Three Center Study
| العنوان: | Evaluating Deep Learning-based Melanoma Classification using Immunohistochemistry and Routine Histology: A Three Center Study |
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| المؤلفون: | Wies, Christoph, Schneider, Lucas, Haggenmueller, Sarah, Bucher, Tabea-Clara, Hobelsberger, Sarah, Heppt, Markus V., Ferrara, Gerardo, Krieghoff-Henning, Eva I., Brinker, Titus J. |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Statistics - Applications |
| الوصف: | 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. |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1371/journal.pone.0297146 |
| الوصول الحر: | http://arxiv.org/abs/2309.03494Test |
| رقم الانضمام: | edsarx.2309.03494 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1371/journal.pone.0297146 |
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