Squelette homotopique 3D pour le traitement et l'analyse du ventricule gauche en SPECT

Des images tomoscintigraphiques sont la représentation 3D de la distribution d'un traceur dans le ventricule gauche (VG). Le but de notre étude est de réussir à quantifier les défauts de fixations (zones d'atténuation) représentatifs d'une pathologie. Le squelette 3D du VG est utilisé pour reconstituer la forme originale du VG. Pour combler les lacunes liées aux pathologies, le squelette est complété conformément à un modèle. Un algorithme fondé sur la théorie de l'incertain (logique floue) utilise le squelette ainsi complété pour générer une nouvelle image avec un contraste plus élevé. Cette nouvelle image peut être plus facilement segmentée, et le VG est considéré dans son intégralité. Le VG peut alors être entièrement quantifié

Truncated Inference for Latent Variable Optimization Problems: Application to Robust Estimation and Learning

Optimization problems with an auxiliary latent variable structure in addition to the main model parameters occur frequently in computer vision and machine learning. The additional latent variables make the underlying optimization task expensive, either in terms of memory (by maintaining the latent variables), or in terms of runtime (repeated exact inference of latent variables). We aim to remove the need to maintain the latent variables and propose two formally justified methods, that dynamically adapt the required accuracy of latent variable inference. These methods have applications in large scale robust estimation and in learning energy-based models from labeled data.Comment: 16 page

Gastrointestinal symptoms and association with medication use patterns, adherence, treatment satisfaction, quality of life, and resource use in osteoporosis: baseline results of the MUSIC-OS study

Summary: The Medication Use Patterns, Treatment Satisfaction, and Inadequate Control of Osteoporosis Study (MUSIC-OS) is a prospective, observational study of women with osteoporosis in Europe and Canada. At baseline, patients with gastrointestinal symptoms reported lower adherence to osteoporosis treatment, treatment satisfaction, and health-related quality of life, than those without gastrointestinal symptoms. Introduction: The aim of the study was to examine gastrointestinal (GI) symptoms and the association between GI symptoms and treatment adherence, treatment satisfaction, and health-related quality of life (HRQoL) among osteoporotic women in Europe and Canada. Methods: Baseline results are reported here for a prospective study which enrolled postmenopausal, osteoporotic women who were initiating (new users) or continuing (experienced users) osteoporosis treatment at study entry (baseline). A patient survey was administered at baseline and included the occurrence of GI symptoms during 6-month pre-enrolment, treatment adherence (adherence evaluation of osteoporosis (ADEOS), score 0–22), treatment satisfaction (Osteoporosis Treatment Satisfaction Questionnaire for Medications (OPSAT-Q), score 0–100) and HRQoL (EuroQol-5 dimension (EQ-5D) utility, score 0–1; OPAQ-SV, score 0–100). The association between GI symptoms and ADEOS (experienced users), OPSAT-Q (experienced users), and HRQoL (new and experienced users) was assessed by general linear models adjusted for patient characteristics. Results: A total of 2959 patients (2275 experienced and 684 new users) were included. Overall, 68.1 % of patients experienced GI symptoms in the past 6 months. Compared with patients without GI symptoms, patients with GI symptoms had lower mean baseline scores on most measures. The mean adjusted differences were ADEOS, −0.43; OPSAT-Q, −5.68; EQ-5D, −0.04 (new users) and −0.06 (experienced users), all P < 0.01. GI symptoms were also associated with lower OPAQ-SV domain scores: physical function, −4.17 (experienced users); emotional status, −4.28 (new users) and −5.68 (experienced users); back pain, −5.82 (new users) and −11.33 (experienced users), all P < 0.01. Conclusions: Patients with GI symptoms have lower treatment adherence and treatment satisfaction and worse HRQoL than patients without GI symptoms

3-dimensional homotopic skeleton for the treatment and analysis of the left ventricule in ERIM spect

National audienc

3-dimensional homotopic skeleton for the treatment and analysis of the left ventricule in ERIM spect

National audienc

Truncated Inference for Latent Variable Optimization Problems: Application to Robust Estimation and Learning

Optimization problems with an auxiliary latent variable structure in addition to the main model parameters occur frequently in computer vision and machine learning. The additional latent variables make the underlying optimization task expensive, either in terms of memory (by maintaining the latent variables), or in terms of runtime (repeated exact inference of latent variables). We aim to remove the need to maintain the latent variables and propose two formally justified methods, that dynamically adapt the required accuracy of latent variable inference. These methods have applications in large scale robust estimation and in learning energy-based models from labeled data

A deep learning framework for neuroscience

Systems neuroscience seeks explanations for how the brain implements a wide variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to design computational systems based on the tasks they will have to solve. In artificial neural networks, the three components specified by design are the objective functions, the learning rules and the architectures. With the growing success of deep learning, which utilizes brain-inspired architectures, these three designed components have increasingly become central to how we model, engineer and optimize complex artificial learning systems. Here we argue that a greater focus on these components would also benefit systems neuroscience. We give examples of how this optimization-based framework can drive theoretical and experimental progress in neuroscience. We contend that this principled perspective on systems neuroscience will help to generate more rapid progress

A deep learning framework for neuroscience

Systems neuroscience seeks explanations for how the brain implements a wide variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to design computational systems based on the tasks they will have to solve. In artificial neural networks, the three components specified by design are the objective functions, the learning rules and the architectures. With the growing success of deep learning, which utilizes brain-inspired architectures, these three designed components have increasingly become central to how we model, engineer and optimize complex artificial learning systems. Here we argue that a greater focus on these components would also benefit systems neuroscience. We give examples of how this optimization-based framework can drive theoretical and experimental progress in neuroscience. We contend that this principled perspective on systems neuroscience will help to generate more rapid progress