IgE allergy diagnostics and other relevant tests in allergy, a World Allergy Organization position paper

Correction: Volume14 Issue7 Article Number100557 DOI10.1016/j.waojou.2021.100557Currently, testing for immunoglobulin E (IgE) sensitization is the cornerstone of diagnostic evaluation in suspected allergic conditions. This review provides a thorough and updated critical appraisal of the most frequently used diagnostic tests, both in vivo and in vitro. It discusses skin tests, challenges, and serological and cellular in vitro tests, and provides an overview of indications, advantages and disadvantages of each in conditions such as respiratory, food, venom, drug, and occupational allergy. Skin prick testing remains the first line approach in most instances; the added value of serum specific IgE to whole allergen extracts or components, as well as the role of basophil activation tests, is evaluated. Unproven, non-validated, diagnostic tests are also discussed. Throughout the review, the reader must bear in mind the relevance of differentiating between sensitization and allergy; the latter entails not only allergic sensitization, but also clinically relevant symptoms triggered by the culprit allergen.Peer reviewe

Fremdwörter unter deutschen und englischen Tiernamen

von J. HeinzerlingIn Fraktu

Die Siedlungen des Kreises Siegen

von J. Heinzerlin

Probe eines Wörterbuches der Siegerländer Mundart

von Jakob HeinzerlingIn FrakturPr. Nr. 36

Zum Gedächtnis des verstorbenen Direktors Prof. Utgenannt

von HeinzerlingProgr. Nr. 48

Model soups improve performance of dermoscopic skin cancer classifiers

Background: Image-based cancer classifiers suffer from a variety of problems which negatively affect their performance. For example, variation in image brightness or different cameras can already suffice to diminish performance. Ensemble solutions, where multiple model predictions are combined into one, can improve these problems. However, ensembles are computationally intensive and less transparent to practitioners than single model solutions. Constructing model soups, by averaging the weights of multiple models into a single model, could circumvent these limitations while still improving performance. Objective: To investigate the performance of model soups for a dermoscopic melanoma-nevus skin cancer classification task with respect to (1) generalisation to images from other clinics, (2) robustness against small image changes and (3) calibration such that the confidences correspond closely to the actual predictive uncertainties. Methods: We construct model soups by fine-tuning pre-trained models on seven different image resolutions and subsequently averaging their weights. Performance is evaluated on a multi-source dataset including holdout and external components. Results: We find that model soups improve generalisation and calibration on the external component while maintaining performance on the holdout component. For robustness, we observe performance improvements for pertubated test images, while the performance on corrupted test images remains on par. Conclusions: Overall, souping for skin cancer classifiers has a positive effect on generalisation, robustness and calibration. It is easy for practitioners to implement and by combining multiple models into a single model, complexity is reduced. This could be an important factor in achieving clinical applicability, as less complexity generally means more transparency. (c) 2022 The Authors. Published by Elsevier Ltd

Explainable artificial intelligence in skin cancer recognition: A systematic review

Background: Due to their ability to solve complex problems, deep neural networks (DNNs) are becoming increasingly popular in medical applications. However, decisionmaking by such algorithms is essentially a black-box process that renders it difficult for physicians to judge whether the decisions are reliable. The use of explainable artificial intelligence We investigate how XAI is used for skin cancer detection: how is it used during the development of new DNNs? What kinds of visualisations are commonly used? Are there systematic Methods: Google Scholar, PubMed, IEEE Explore, Science Direct and Scopus were searched for peer-reviewed studies published between January 2017 and October 2021 applying XAI to dermatological images: the search terms histopathological image, whole-slide image, clinical image, dermoscopic image, skin, dermatology, explainable, interpretable and XAI were used isting XAI methods to their classifier to interpret its decision-making. Some studies (4/37) proposed new XAI methods or improved upon existing techniques. 14/37 studies addressed specific questions such as bias detection and impact of XAI on man-machine-interactions. However, only three of them evaluated the performance and confidence of humans using Conclusion: XAI is commonly applied during the development of DNNs for skin cancer detection. However, a systematic and rigorous evaluation of its usefulness in this scenario is lacking. 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the C