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is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2110.09525">arXiv:2110.09525</a> <span> [<a href="https://arxiv.org/pdf/2110.09525">pdf</a>, <a href="https://arxiv.org/format/2110.09525">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Eigenbehaviour as an Indicator of Cognitive Abilities </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Botros%2C+A">Angela Botros</a>, <a href="/search/cs?searchtype=author&query=Sch%C3%BCtz%2C+N">Narayan Sch眉tz</a>, <a href="/search/cs?searchtype=author&query=R%C3%B6cke%2C+C">Christina R枚cke</a>, <a href="/search/cs?searchtype=author&query=Weibel%2C+R">Robert Weibel</a>, <a href="/search/cs?searchtype=author&query=Martin%2C+M">Mike Martin</a>, <a href="/search/cs?searchtype=author&query=M%C3%BCri%2C+R">Ren茅 M眉ri</a>, <a href="/search/cs?searchtype=author&query=Nef%2C+T">Tobias Nef</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2110.09525v1-abstract-short" style="display: inline;"> With growing usage of machine learning algorithms and big data in health applications, digital biomarkers have become an important key feature to ensure the success of those applications. In this paper, we focus on one important use-case, the long-term continuous monitoring of the cognitive ability of older adults. The cognitive ability is a factor both for long-term monitoring of people living al… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.09525v1-abstract-full').style.display = 'inline'; document.getElementById('2110.09525v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2110.09525v1-abstract-full" style="display: none;"> With growing usage of machine learning algorithms and big data in health applications, digital biomarkers have become an important key feature to ensure the success of those applications. In this paper, we focus on one important use-case, the long-term continuous monitoring of the cognitive ability of older adults. The cognitive ability is a factor both for long-term monitoring of people living alone as well as an outcome in clinical studies. In this work, we propose a new digital biomarker for cognitive abilities based on location eigenbehaviour obtained from contactless ambient sensors. Indoor location information obtained from passive infrared sensors is used to build a location matrix covering several weeks of measurement. Based on the eigenvectors of this matrix, the reconstruction error is calculated for various numbers of used eigenvectors. The reconstruction error is used to predict cognitive ability scores collected at baseline, using linear regression. Additionally, classification of normal versus pathological cognition level is performed using a support-vector-machine. Prediction performance is strong for high levels of cognitive ability, but grows weaker for low levels of cognitive ability. Classification into normal versus pathological cognitive ability level reaches high accuracy with a AUC = 0.94. Due to the unobtrusive method of measurement based on contactless ambient sensors, this digital biomarker of cognitive ability is easily obtainable. The usage of the reconstruction error is a strong digital biomarker for the binary classification and, to a lesser extent, for more detailed prediction of interindividual differences in cognition. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.09525v1-abstract-full').style.display = 'none'; document.getElementById('2110.09525v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 October, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1904.03041">arXiv:1904.03041</a> <span> [<a href="https://arxiv.org/pdf/1904.03041">pdf</a>, <a href="https://arxiv.org/format/1904.03041">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=McKinley%2C+R">Richard McKinley</a>, <a href="/search/cs?searchtype=author&query=Grunder%2C+L">Lorenz Grunder</a>, <a href="/search/cs?searchtype=author&query=Wepfer%2C+R">Rik Wepfer</a>, <a href="/search/cs?searchtype=author&query=Aschwanden%2C+F">Fabian Aschwanden</a>, <a href="/search/cs?searchtype=author&query=Fischer%2C+T">Tim Fischer</a>, <a href="/search/cs?searchtype=author&query=Friedli%2C+C">Christoph Friedli</a>, <a href="/search/cs?searchtype=author&query=Muri%2C+R">Raphaela Muri</a>, <a href="/search/cs?searchtype=author&query=Rummel%2C+C">Christian Rummel</a>, <a href="/search/cs?searchtype=author&query=Verma%2C+R">Rajeev Verma</a>, <a href="/search/cs?searchtype=author&query=Weisstanner%2C+C">Christian Weisstanner</a>, <a href="/search/cs?searchtype=author&query=Reyes%2C+M">Mauricio Reyes</a>, <a href="/search/cs?searchtype=author&query=Salmen%2C+A">Anke Salmen</a>, <a href="/search/cs?searchtype=author&query=Chan%2C+A">Andrew Chan</a>, <a href="/search/cs?searchtype=author&query=Wiest%2C+R">Roland Wiest</a>, <a href="/search/cs?searchtype=author&query=Wagner%2C+F">Franca Wagner</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1904.03041v1-abstract-short" style="display: inline;"> The detection of new or enlarged white-matter lesions in multiple sclerosis is a vital task in the monitoring of patients undergoing disease-modifying treatment for multiple sclerosis. However, the definition of 'new or enlarged' is not fixed, and it is known that lesion-counting is highly subjective, with high degree of inter- and intra-rater variability. Automated methods for lesion quantificati… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1904.03041v1-abstract-full').style.display = 'inline'; document.getElementById('1904.03041v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1904.03041v1-abstract-full" style="display: none;"> The detection of new or enlarged white-matter lesions in multiple sclerosis is a vital task in the monitoring of patients undergoing disease-modifying treatment for multiple sclerosis. However, the definition of 'new or enlarged' is not fixed, and it is known that lesion-counting is highly subjective, with high degree of inter- and intra-rater variability. Automated methods for lesion quantification hold the potential to make the detection of new and enlarged lesions consistent and repeatable. However, the majority of lesion segmentation algorithms are not evaluated for their ability to separate progressive from stable patients, despite this being a pressing clinical use-case. In this paper we show that change in volumetric measurements of lesion load alone is not a good method for performing this separation, even for highly performing segmentation methods. Instead, we propose a method for identifying lesion changes of high certainty, and establish on a dataset of longitudinal multiple sclerosis cases that this method is able to separate progressive from stable timepoints with a very high level of discrimination (AUC = 0.99), while changes in lesion volume are much less able to perform this separation (AUC = 0.71). Validation of the method on a second external dataset confirms that the method is able to generalize beyond the setting in which it was trained, achieving an accuracy of 83% in separating stable and progressive timepoints. Both lesion volume and count have previously been shown to be strong predictors of disease course across a population. However, we demonstrate that for individual patients, changes in these measures are not an adequate means of establishing no evidence of disease activity. Meanwhile, directly detecting tissue which changes, with high confidence, from non-lesion to lesion is a feasible methodology for identifying radiologically active patients. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1904.03041v1-abstract-full').style.display = 'none'; document.getElementById('1904.03041v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 April, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1901.07419">arXiv:1901.07419</a> <span> [<a href="https://arxiv.org/pdf/1901.07419">pdf</a>, <a href="https://arxiv.org/format/1901.07419">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Simultaneous lesion and neuroanatomy segmentation in Multiple Sclerosis using deep neural networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=McKinley%2C+R">Richard McKinley</a>, <a href="/search/cs?searchtype=author&query=Wepfer%2C+R">Rik Wepfer</a>, <a href="/search/cs?searchtype=author&query=Aschwanden%2C+F">Fabian Aschwanden</a>, <a href="/search/cs?searchtype=author&query=Grunder%2C+L">Lorenz Grunder</a>, <a href="/search/cs?searchtype=author&query=Muri%2C+R">Raphaela Muri</a>, <a href="/search/cs?searchtype=author&query=Rummel%2C+C">Christian Rummel</a>, <a href="/search/cs?searchtype=author&query=Verma%2C+R">Rajeev Verma</a>, <a href="/search/cs?searchtype=author&query=Weisstanner%2C+C">Christian Weisstanner</a>, <a href="/search/cs?searchtype=author&query=Reyes%2C+M">Mauricio Reyes</a>, <a href="/search/cs?searchtype=author&query=Salmen%2C+A">Anke Salmen</a>, <a href="/search/cs?searchtype=author&query=Chan%2C+A">Andrew Chan</a>, <a href="/search/cs?searchtype=author&query=Wagner%2C+F">Franca Wagner</a>, <a href="/search/cs?searchtype=author&query=Wiest%2C+R">Roland Wiest</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1901.07419v3-abstract-short" style="display: inline;"> Segmentation of white matter lesions and deep grey matter structures is an important task in the quantification of magnetic resonance imaging in multiple sclerosis. In this paper we explore segmentation solutions based on convolutional neural networks (CNNs) for providing fast, reliable segmentations of lesions and grey-matter structures in multi-modal MR imaging, and the performance of these meth… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1901.07419v3-abstract-full').style.display = 'inline'; document.getElementById('1901.07419v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1901.07419v3-abstract-full" style="display: none;"> Segmentation of white matter lesions and deep grey matter structures is an important task in the quantification of magnetic resonance imaging in multiple sclerosis. In this paper we explore segmentation solutions based on convolutional neural networks (CNNs) for providing fast, reliable segmentations of lesions and grey-matter structures in multi-modal MR imaging, and the performance of these methods when applied to out-of-centre data. We trained two state-of-the-art fully convolutional CNN architectures on the 2016 MSSEG training dataset, which was annotated by seven independent human raters: a reference implementation of a 3D Unet, and a more recently proposed 3D-to-2D architecture (DeepSCAN). We then retrained those methods on a larger dataset from a single centre, with and without labels for other brain structures. We quantified changes in performance owing to dataset shift, and changes in performance by adding the additional brain-structure labels. We also compared performance with freely available reference methods. Both fully-convolutional CNN methods substantially outperform other approaches in the literature when trained and evaluated in cross-validation on the MSSEG dataset, showing agreement with human raters in the range of human inter-rater variability. Both architectures showed drops in performance when trained on single-centre data and tested on the MSSEG dataset. When trained with the addition of weak anatomical labels derived from Freesurfer, the performance of the 3D Unet degraded, while the performance of the DeepSCAN net improved. Overall, the DeepSCAN network predicting both lesion and anatomical labels was the best-performing network examined. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1901.07419v3-abstract-full').style.display = 'none'; document.getElementById('1901.07419v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 November, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 22 January, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Substantially revised version after comments from reviewers, including comparison to 3D Unet</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" 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