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Henning Müller
University of Applied Sciences Western Switzerland
Data miningImage retrievalInformation retrievalComputer visionComputer science
495Publications
47H-index
9,122Citations
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Publications 520
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#1Mara Graziani (University of Geneva)
Last. Henning Müller (University of Geneva)H-Index: 47
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Abstract Deep learning explainability is often reached by gradient-based approaches that attribute the network output to perturbations of the input pixels. However, the relevance of input pixels may be difficult to relate to relevant image features in some applications, eg. diagnostic measures in medical imaging. The framework described in this paper shifts the attribution focus from pixel values to user-defined concepts. By checking if certain diagnostic measures are present in the learned repr...
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#1Anjani Dhrangadhariya (University of Applied Sciences Western Switzerland)
#2Roger Hilfiker (RMIT: RMIT University)H-Index: 19
Last. Henning Müller (University of Applied Sciences Western Switzerland)H-Index: 47
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Evidence-based practice is highly dependent upon up-to-date systematic reviews (SR) for decision making. However, conducting and updating systematic reviews, especially the citation screening for identification of relevant studies, requires much human work and is therefore expensive. Automating citation screening using machine learning (ML) based approaches can reduce cost and labor. Machine learning has been applied to automate citation screening but not for the SRs with very narrow research qu...
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#1Anjani Dhrangadhariya (University of Applied Sciences Western Switzerland)
#2Sandy Millius (University of Applied Sciences Western Switzerland)
Last. Hugues BratH-Index: 1
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Radiology reports describe the findings of a radiologist in an imaging examination, produced for another clinician in order to answer to a clinical indication. Sometimes, the report does not fully answer the question asked, despite guidelines for the radiologist. In this article, a system that controls the quality of reports automatically is described. It notably maps the free text onto MeSH terms and checks if the anatomy and disease terms match in the indication and conclusion of a report. The...
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The Image Biomarker Standardisation Initiative (IBSI) aims to improve reproducibility of radiomics studies by standardising the computational process of extracting image biomarkers (features) from images. We have previously established reference values for 169 commonly used features, created a standard radiomics image processing scheme, and developed reporting guidelines for radiomic studies. However, several aspects are not standardised. Here we present a preliminary version of a reference manu...
#1Henning Müller (University of Applied Sciences Western Switzerland)H-Index: 47
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Apr 14, 2020 in ECIR (European Conference on Information Retrieval)
#1Alexis JolyH-Index: 22
#2Hervé GoëauH-Index: 16
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Building accurate knowledge of the identity, the geographic distribution and the evolution of species is essential for the sustainable development of humanity, as well as for biodiversity conservation. However, the difficulty of identifying plants and animals in the field is hindering the aggregation of new data and knowledge. Identifying and naming living plants or animals is almost impossible for the general public and is often difficult even for professionals and naturalists. Bridging this ga...
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#1Sebastian OtáloraH-Index: 5
#2Manfredo AtzoriH-Index: 1
Last. Henning MüllerH-Index: 47
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1 CitationsSource
#1Amjad P. KhanH-Index: 16
#2Manfredo AtzoriH-Index: 1
Last. Henning MüllerH-Index: 47
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#1Katharina HoebelH-Index: 1
#2Vincent Andrearczyk (University of Applied Sciences Western Switzerland)H-Index: 5
Last. Jayashree Kalpathy-CramerH-Index: 29
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Including uncertainty information in the assessment of a segmentation of pathologic structures on medical images, offers the potential to increase trust into deep learning algorithms for the analysis of medical imaging. Here, we examine options to extract uncertainty information from deep learning segmentation models and the influence of the choice of cost functions on these uncertainty measures. To this end we train conventional UNets without dropout, deep UNet ensembles, and Monte-Carlo (MC) d...
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Last. Henning MüllerH-Index: 47
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The overall lower survival rate of patients with rare cancers can be explained, among other factors, by the limitations resulting from the scarce available information about them. Large biomedical data repositories, such as PubMed Central Open Access (PMC-OA), have been made freely available to the scientific community and could be exploited to advance the clinical assessment of these diseases. A multimodal approach using visual deep learning and natural language processing methods was developed...
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