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Henning Müller

Researcher at University of Applied Sciences Western Switzerland

Publications -  564
Citations -  18705

Henning Müller is an academic researcher from University of Applied Sciences Western Switzerland. The author has contributed to research in topics: Image retrieval & Visual Word. The author has an hindex of 61, co-authored 532 publications receiving 15234 citations. Previous affiliations of Henning Müller include École Polytechnique Fédérale de Lausanne & Geneva College.

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The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

Alex Zwanenburg, +70 more
- 01 May 2020 - 
TL;DR: A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software and could be excellently reproduced.
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A review of content-based image retrieval systems in medical applications—clinical benefits and future directions

TL;DR: The goal is not, in general, to replace text-based retrieval methods as they exist at the moment but to complement them with visual search tools.
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Performance evaluation in content-based image retrieval: overview and proposals

TL;DR: The advantages and shortcomings of the performance measures currently used in CBIR are discussed and proposals for a standard test suite similar to that used in IR at the annual Text REtrieval Conference (TREC), are presented.
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Electromyography data for non-invasive naturally-controlled robotic hand prostheses

TL;DR: This work aims to close this gap by allowing worldwide research groups to develop and test movement recognition and force control algorithms on a benchmark scientific database, with the final goal of developing non-invasive, naturally controlled, robotic hand prostheses.
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Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands

TL;DR: The results show that convolutional neural networks with a very simple architecture can produce accurate results comparable to the average classical classification methods, and show that several factors can be fundamental for the analysis of sEMG data.