Christian Scheibböck

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BACKGROUND Medical diagnosis and prognosis using machine learning methods is usually represented as a supervised classification problem, where a model is built to distinguish "normal" from "abnormal" cases. If cases are available from only one class, this approach is not feasible. OBJECTIVE To evaluate the performance of classification via outlier(More)
We evaluated the accuracy of diagnoses made from pictures taken with the built-in cameras of mobile phones in a 'real-life' clinical setting. A total of 263 patients took part, who photographed their own lesions where possible, and provided clinical information via a questionnaire. After the teledermatology procedure, each patient was examined face-to-face(More)
Malignant melanoma of the skin potentially leads to widespread metastasis. Prediction of metasta-sis could be improved by using tumour markers. A reduction of additional diagnostic workup potentially increases well-being of the patients and may reduce costs. The challenge is to design a knowledge based system for the prediction of metastasis, by combining(More)
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