Folner Conditions, Nuclearity, and Subexponential Growth inC*-Algebras

AbstractAnswering a first question of Voiculescu (On the existence of quasi-central approximate units relative to normed ideals,J. Funct. Anal.91(1990), 1–36) Vaillant 8?and Kirchberg (OnC*-algebras having subexponential, polynomial and linear growth,Invent. Math.108(1992), 635–652) showed that subexponential growth implies nuclearity in theC*-context. Voiculescu suggested a Følner type condition forC*-algebras and asked about the relation to growth and nuclearity. In this work we clarify the relation among subexponential growth phenomena, this Følner condition suggested by Voiculescu, weak filtrability in the sense of Arveson and Bedos, and nuclearity in theC*-algebra context

Advances in the Clinica software platform for clinical neuroimaging studies

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Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

Background: Cardiovascular magnetic resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accurate quantification of the cardiac chamber volume, ejection fraction and myocardial mass, providing information for diagnosis and monitoring of CVDs. However, for years, clinicians have been relying on manual approaches for CMR image analysis, which is time consuming and prone to subjective errors. It is a major clinical challenge to automatically derive quantitative and clinically relevant information from CMR images. Methods: Deep neural networks have shown a great potential in image pattern recognition and segmentation for a variety of tasks. Here we demonstrate an automated analysis method for CMR images, which is based on a fully convolutional network (FCN). The network is trained and evaluated on a large-scale dataset from the UK Biobank, consisting of 4,875 subjects with 93,500 pixelwise annotated images. The performance of the method has been evaluated using a number of technical metrics, including the Dice metric, mean contour distance and Hausdorff distance, as well as clinically relevant measures, including left ventricle (LV) end-diastolic volume (LVEDV) and end-systolic volume (LVESV), LV mass (LVM); right ventricle (RV) end-diastolic volume (RVEDV) and end-systolic volume (RVESV). Results: By combining FCN with a large-scale annotated dataset, the proposed automated method achieves a high performance in segmenting the LV and RV on short-axis CMR images and the left atrium (LA) and right atrium (RA) on long-axis CMR images. On a short-axis image test set of 600 subjects, it achieves an average Dice metric of 0.94 for the LV cavity, 0.88 for the LV myocardium and 0.90 for the RV cavity. The mean absolute difference between automated measurement and manual measurement was 6.1 mL for LVEDV, 5.3 mL for LVESV, 6.9 gram for LVM, 8.5 mL for RVEDV and 7.2 mL for RVESV. On long-axis image test sets, the average Dice metric was 0.93 for the LA cavity (2-chamber view), 0.95 for the LA cavity (4-chamber view) and 0.96 for the RA cavity (4-chamber view). The performance is comparable to human inter-observer variability. Conclusions: We show that an automated method achieves a performance on par with human experts in analysing CMR images and deriving clinically relevant measures

Comparing NER approaches on French clinical text, with easy-to-reuse pipelines

International audienceThe task of Named Entity Recognition (NER) is central for leveraging the content of clinical texts in observational studies. Indeed, texts contain a large part of the information available in Electronic Health Records (EHRs). However, clinical texts are highly heterogeneous between healthcare services and institutions, between countries and languages, making it hard to predict how existing tools may perform on a particular corpus. We compared four NER approaches on three French corpora and share our benchmarking pipeline in an open and easy-to-reuse manner, using the medkit Python library. We include in our pipelines fine-tuning operations with either one or several of the considered corpora. Our results illustrate the expected superiority of language models over a dictionary-based approach, and question the necessity of refining models already trained on biomedical texts. Beyond benchmarking, we believe sharing reusable and customizable pipelines for comparing fast-evolving Natural Language Processing (NLP) tools is a valuable contribution, since clinical texts themselves can hardly be shared for privacy concerns

From Nipype to Pydra: a Clinica story

International audienceCarrying out neuroimaging studies often involves multiple steps requiring different tools. As such it can be difficult to reproduce an experiment. Clinica (Routier et al., 2021) is a software platform that aims at empowering users with an environment to make reproducibility possible. In this abstract, we describe Clinica's migration to Pydra, a modern dataflow engine developed for the Nipype project (https://nipype.github.io/pydra). We also highlight our wider contributions to the Pydra ecosystem which will benefit the community at large

Advances in the Clinica software platform for clinical neuroimaging studies

International audienc