Background: Multiple studies have compared the performance of artificial intelligence (AI)ebased models for automated skin cancer classification to human experts, thus setting the cornerstone for a successful translation of AI-based tools into clinicopathological practice.

Background: Clinicians and pathologists traditionally use patient data in addition to clinical examination to support their diagnoses. Objectives: We investigated whether a combination of histologic whole slides image (WSI) analysis based on convolutional neural networks (CNNs) and commonly available patient data (age, sex and anatomical site of the lesion) in a binary melanoma/nevus classification task could increase the performance compared with CNNs alone. Methods: We used 431 WSIs from two different laboratories and analysed the performance of classifiers that used the image or patient data individually or three common fusion techniques. Furthermore, we tested a naive combination of patient data and an image classifier: for cases interpreted as 'uncertain' (CNN output score <0.7), the decision of the CNN was replaced by the decision of the patient data classifier. Results: The CNN on its own achieved the best performance (mean AE standard deviation of five individual runs) with AUROC of 92.30% AE 0.23% and balanced accuracy of 83.17% AE 0.38%. While the classification performance was not significantly improved in general by any of the tested fusions, naive strategy of replacing the image classifier with the patient data classifier on slides with low output scores improved balanced accuracy to 86.72% AE 0.36%. Conclusion:In most cases, the CNN on its own was so accurate that patient data integration did not provide any benefit. However, incorporating patient data for lesions that were classified by the CNN with low 'confidence' improved balanced accuracy.

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Background: One prominent application for deep learningebased classifiers is skin cancer classification on dermoscopic images. However, classifier evaluation is often limited to holdout data which can mask common shortcomings such as susceptibility to confounding factors. To increase clinical applicability, it is necessary to thoroughly evaluate such classifiers on out-of-distribution (OOD) data. Objective: The objective of the study was to establish a dermoscopic skin cancer benchmark in which classifier robustness to OOD data can be measured. Methods: Using a proprietary dermoscopic image database and a set of image transformations, we create an OOD robustness benchmark and evaluate the robustness of four different convolutional neural network (CNN) architectures on it. Results: The benchmark contains three data setsdSkin Archive Munich (SAM), SAM-corrupted (SAM-C) and SAM-perturbed (SAM-P)dand is publicly available for download. To maintain the benchmark's OOD status, ground truth labels are not provided and test results should be sent to us for assessment. The SAM data set contains 319 unmodified and biopsy-verified dermoscopic melanoma (n Z 194) and nevus (n Z 125) images. SAM-C and SAM-P contain images from SAM which were artificially modified to test a classifier against low-quality inputs and to measure its prediction stability over small image changes, respectively. All four CNNs showed susceptibility to corruptions and perturbations. Conclusions: This benchmark provides three data sets which allow for OOD testing of binary skin cancer classifiers. Our classifier performance confirms the shortcomings of CNNs and provides a frame of reference. Altogether, this benchmark should facilitate a more thorough evaluation process and thereby enable the development of more robust skin cancer classifiers.

--ZusammenfassungCheck Character Systems. A hopefully complete review is given of different decimal codes that are in use. The grouptheoretical background of those methods is explained and applied to the construction of new error detecting codes. With the help of Latin squares (or quasi-groups) new error codes are found, especially a good decimal shift-code.Priifzeiehen-Verfahren. Wir geben einen m6glichst vollstfindigen Oberblick fiber alle gebrfiuchlichen Priifziffern-Verfahren. Der gruppentheoretische Hintergrund aller dieser Verfahren wird erklgrt, und es wird gezeigt, wie sich auf dieser Basis neue Prtifzeichen-Verfahren konstruieren lassen. Mit Hilfe lateinischer Quadrate (oder Quasigruppen) werden neue Priifzeichen-Verfahren gefunden, speziell ein guter dezimaler Shift-Code.