DeepCoder: Semi-parametric Variational Autoencoders for Automatic Facial Action Coding

Human face exhibits an inherent hierarchy in its representations (i.e., holistic facial expressions can be encoded via a set of facial action units (AUs) and their intensity). Variational (deep) auto-encoders (VAE) have shown great results in unsupervised extraction of hierarchical latent representations from large amounts of image data, while being robust to noise and other undesired artifacts. Potentially, this makes VAEs a suitable approach for learning facial features for AU intensity estimation. Yet, most existing VAE-based methods apply classifiers learned separately from the encoded features. By contrast, the non-parametric (probabilistic) approaches, such as Gaussian Processes (GPs), typically outperform their parametric counterparts, but cannot deal easily with large amounts of data. To this end, we propose a novel VAE semi-parametric modeling framework, named DeepCoder, which combines the modeling power of parametric (convolutional) and nonparametric (ordinal GPs) VAEs, for joint learning of (1) latent representations at multiple levels in a task hierarchy1, and (2) classification of multiple ordinal outputs. We show on benchmark datasets for AU intensity estimation that the proposed DeepCoder outperforms the state-of-the-art approaches, and related VAEs and deep learning models.Comment: ICCV 2017 - accepte

Interleukin-6 is not essential for bone turnover in hypothyroid mice.

Interleukin-6 (IL-6) has been shown to be involved in the pathogenesis of several bone diseases characterized by an imbalance between bone resorption and formation. The aim of the study was to estimate serum markers of bone turnover: osteoclast-derived tartrate-resistant acid phosphatase form 5a (TRACP 5b) and osteocalcin in IL-6-deficient mice to assess the role of IL-6 in bone metabolism in hypothyroidism in mice. C57BL/6J (wild-type; WT) and C57BL/6J(IL6-/-Kopf) (IL-6 knock-out; IL6KO) mice randomly divided into 4 groups with 10 in each one: 1/ WT mice in hypothyroidism (WT-ht), 2/ WT controls, 3/ IL6KO mice with hypothyroidism (IL6KO-ht) and 4/ IL6KO controls. Experimental model of hypothyroidism was induced by intraperitoneal injection of propylthiouracyl. The serum levels of TRACP 5b and osteocalcin were determined by ELISA. Serum concentrations of TRACP 5b (median and interquartile ranges) were significantly decreased in both groups of mice with hypothyroidism: WT (3.2 (2.5-4.7) U/l) and IL6KO (2.6 (1.8-3.5) U/l) as compared to the respective controls. Similarly, serum osteocalcin levels were significantly reduced in both groups of mice in experimental hypothyroidism: WT (25.8 (23.0-28.2) ng/ml) and IL6KO (21.5(19.0-24.6) ng/ml) in comparison to the respective controls. There were no significant differences in bone turnover markers between IL6KO and WT mice both in hypothyroid and control animals. The results of the present study suggest that IL-6 does not play an important role in bone turnover in both euthyroid and hypothyroid mice

Recommendations of the Polish Medical Society of Radiology and the Polish Society of Neurology for the routinely used magnetic resonance imaging protocol in patients with multiple sclerosis

Magnetic resonance imaging (MRI) is a widely used method for the diagnosis of multiple sclerosis (MS) that is essential for the detection and follow-up of the disease. The Polish Medical Society of Radiology (PLTR) and the Polish Society of Neurology (PTN) present the second version of the recommendations for examinations routinely conducted in magnetic resonance imaging departments in patients with MS, which include new data and practical comments for electroradiology technicians and radiologists. The recommended protocol aims to improve the MRI procedure and, most importantly, to standardise the method of conducting scans in all MRI departments. This is crucial for the initial diagnostics that are necessary to establish a diagnosis as well as monitor patients with MS, which directly translates into significant clinical decisions. MS is a chronic idiopathic inflammatory demyelinating disease of the central nervous system (CNS), the aetiology of which is still unknown. The nature of the disease lies in the CNS destruction process disseminated in time and space. MRI detects focal lesions in the white and grey matter with high sensitivity (with significantly less specificity in the latter). It is also the best tool to assess brain atrophy in patients with MS in terms of grey matter volume and white matter volume as well as local atrophy (by measuring the volume of thalamus, corpus callosum, subcortical nuclei, hippocampus) as parameters that correlate with disability progression and cognitive dysfunctions. Progress in magnetic resonance techniques, as well as the abilities of postprocessing the obtained data, has become the basis for the dynamic development of computer programs that allow for a more repeatable assessment of brain atrophy in both cross-sectional and longitudinal studies. MRI is unquestionably the best diagnostic tool used to follow up the course of the disease and to treat patients with MS. However, to diagnose and follow up the patients with MS on the basis of MRI in accordance with the latest standards, an MRI study must meet certain quality criteria, which are the subject of this paper

Recommendations of the Polish Medical Society of Radiology and the Polish Society of Neurology for a protocol concerning routinely used magnetic resonance imaging in patients with multiple sclerosis

Magnetic resonance imaging (MRI) is a widely used method for the diagnosis of multiple sclerosis that is essential for the detection and follow-up of the disease.Objective: The Polish Medical Society of Radiology (PLTR) and the Polish Society of Neurology (PTN) present the second version of their recommendations for investigations routinely conducted in magnetic resonance imaging departments in patients with multiple sclerosis. This version includes new data and practical comments for electroradiology technologists and radiologists. The recommended protocol aims to improve the MRI procedure and, most importantly, to standardise the method of conducting scans in all MRI departments. This is crucial for the initial diagnostics necessary for establishing a diagnosis, as well as for MS patient monitoring, which directly translates into significant clinical decisions.Introduction: Multiple sclerosis (MS) is a chronic immune mediated inflammatory demyelinating disease of the central nervous system (CNS), the aetiology of which is still unknown. The nature of the disease lies in a CNS destruction process disseminated in time (DIT) and space (DIS). MRI detects focal lesions in the white and grey matter with high sensitivity (although with significantly lower specificity in the latter). It is also the best tool to assess brain atrophy in patients with MS in terms of grey matter volume (GMV) and white matter volume (WMV) as well as local atrophy (by measuring the volume of thalamus, corpus callosum, subcortical nuclei, and hippocampus) as parameters that correlate with disability progression and cognitive dysfunctions. Progress in MR techniques, as well as advances in postprocessing the obtained data, has driven the dynamic development of computer programs that allow for a more repeatable assessment of brain atrophy in both cross-sectional and longitudinal studies. MR imaging is unquestionably the best diagnostic tool available to follow up the course of the disease and support clinicians in choosing the most appropriate treatment strategy for their MS patient. However, to diagnose and follow up MS patients on the basis of MRI in accordance with the latest standards, the MRI study must adhere to certain quality criteria. Such criteria are the subject of this paper

Variability and magnitude of brain glutamate levels in schizophrenia: a meta and mega-analysis

Glutamatergic dysfunction is implicated in schizophrenia pathoaetiology, but this may vary in extent between patients. It is unclear whether inter-individual variability in glutamate is greater in schizophrenia than the general population. We conducted meta-analyses to assess (1) variability of glutamate measures in patients relative to controls (log coefficient of variation ratio: CVR); (2) standardised mean differences (SMD) using Hedges g; (3) modal distribution of individual-level glutamate data (Hartigan’s unimodality dip test). MEDLINE and EMBASE databases were searched from inception to September 2022 for proton magnetic resonance spectroscopy (1H-MRS) studies reporting glutamate, glutamine or Glx in schizophrenia. 123 studies reporting on 8256 patients and 7532 controls were included. Compared with controls, patients demonstrated greater variability in glutamatergic metabolites in the medial frontal cortex (MFC, glutamate: CVR = 0.15, p < 0.001; glutamine: CVR = 0.15, p = 0.003; Glx: CVR = 0.11, p = 0.002), dorsolateral prefrontal cortex (glutamine: CVR = 0.14, p = 0.05; Glx: CVR = 0.25, p < 0.001) and thalamus (glutamate: CVR = 0.16, p = 0.008; Glx: CVR = 0.19, p = 0.008). Studies in younger, more symptomatic patients were associated with greater variability in the basal ganglia (BG glutamate with age: z = −0.03, p = 0.003, symptoms: z = 0.007, p = 0.02) and temporal lobe (glutamate with age: z = −0.03, p = 0.02), while studies with older, more symptomatic patients associated with greater variability in MFC (glutamate with age: z = 0.01, p = 0.02, glutamine with symptoms: z = 0.01, p = 0.02). For individual patient data, most studies showed a unimodal distribution of glutamatergic metabolites. Meta-analysis of mean differences found lower MFC glutamate (g = −0.15, p = 0.03), higher thalamic glutamine (g = 0.53, p < 0.001) and higher BG Glx in patients relative to controls (g = 0.28, p < 0.001). Proportion of males was negatively associated with MFC glutamate (z = −0.02, p < 0.001) and frontal white matter Glx (z = −0.03, p = 0.02) in patients relative to controls. Patient PANSS total score was positively associated with glutamate SMD in BG (z = 0.01, p = 0.01) and temporal lobe (z = 0.05, p = 0.008). Further research into the mechanisms underlying greater glutamatergic metabolite variability in schizophrenia and their clinical consequences may inform the identification of patient subgroups for future treatment strategies

Structured machine learning methods for automated analysis of facial expressions

Automated recognition of facial expressions, and detection of facial action units (AUs) from videos depends critically on modeling of their dynamics. Some of these dynamics are characterized by changes in temporal phases (onset-apex-offset) and intensity of emotion expressions and AUs. The appearance of these changes may vary considerably among subjects, making the recognition/detection task very challenging. Recent advances in deep neural networks (DNN) and, in particular, convolutional models have facilitated “end-to-end” learning and reduced or even completely eliminated the dependence and need for physics-based models and/or other pre-processing techniques. While the effect- iveness of these models has been demonstrated on many computer vision problems, only baseline tasks such as expression recognition, AU detection and AU intensity estimation have been investigated. The structure of facial expressions arises from statistically induced co-occurrence patterns of AU intensity levels. Our goal is to model this structure by combining conditional random fields (CRF) with deep learning. The contribution of this thesis is two-fold. First, we introduce a novel Latent-CRF model for classification of image sequences. Second, we propose a deep probabilistic framework for modeling multivariate ordinal variables. Latent-CRFs efficiently encode dynamics through latent states accounting for temporal consistency. These latent states are typically assumed to be either unordered (nominal) or fully ordered (ordinal). Yet, while the video segments containing activation of the target AU may better be described using ordinal latent states (corresponding to the AU intensity levels), the segments where this AU does not occur, may better be described using unordered (nominal) latent states. To address this, we propose the Variable-state L-CRF model that automatically selects the optimal latent states for the target image sequence, based on the input data and underlying dynamics of the sequence. The deep probabilistic framework introduced in the second part of this thesis accounts for ordinal structure in the output variables and their non-linear dependencies via Copula functions modeled as cliques of a CRF. These are jointly optimized with deep CNN feature encoding layers using a newly introduced balanced batch iterative training algorithm. We show that joint learning of the deep features and the target output structure results in significant performance gains compared to existing deep structured models for analysis of facial expressions. We show that the proposed models consistently outperforms (i) independent modeling of AU intensities and (ii) the state-of-the-art approach for the target task and (iii) deep convolutional neural networks.Open Acces