Copula Ordinal Regression for Joint Estimation of Facial Action Unit Intensity

Joint modeling of the intensity of facial action units (AUs) from face images is challenging due to the large number of AUs (30+) and their intensity levels (6). This is in part due to the lack of suitable models that can efficiently handle such a large number of outputs/classes simultaneously, but also due to the lack of labelled target data. For this reason, majority of the methods proposed so far resort to independent classifiers for the AU intensity. This is suboptimal for at least two reasons: the facial appearance of some AUs changes depending on the intensity of other AUs, and some AUs co-occur more often than others. Encoding this is expected to improve the estimation of target AU intensities, especially in the case of noisy image features, head-pose variations and imbalanced training data. To this end, we introduce a novel modeling framework, Copula Ordinal Regression (COR), that leverages the power of copula functions and CRFs, to detangle the probabilistic modeling of AU dependencies from the marginal modeling of the AU intensity. Consequently, the COR model achieves the joint learning and inference of intensities of multiple AUs, while being computationally tractable. We show on two challenging datasets of naturalistic facial expressions that the proposed approach consistently outperforms (i) independent modeling of AU intensities, and (ii) the state-ofthe-art approach for the target task

Evaluation of radiological and clinical efficacy of ^{90}Y-DOTATATE} therapy in patients with progressive metastatic midgut neuroendocrine carcinomas

Background: To evaluate the radiological and clinical therapeutic effectiveness of ^{90}Y-octreotate [DOTATATE] inpatients with progressive somatostatin receptor-positive midgut neuroendocrine carcinomas (GEPNETs). Material/Methods: The study group: 34 patients, with histological proven extensive non-resectable and progressive midgut GEP-NETs. Radionuclide therapy (^{90}Y-DOTATATE) was given i.v. with a mean activity per administration 3,82 GBq. Initial clinical tumor responses were assessed 6-7 weeks after therapy completion and then once 3-monthly. The objective tumor response was classified according to the RECIST, initially between 4-6 months and then after each of the 6 months interval. Results: At 6 months after treatment completion, radiological tumor response was observed in 6 subjects with PR (19%), 25 presented SD (78%) and single had PD (3%). Overall clinical response to therapy at 6 months follow-up was observed in 23 patients (68%), SD in 5 patients (15%) and PD in 6 (18%). A year after therapy radiological tumour response was seen in 11 patients (44%), SD had 12 subjects (44%) and DP was noted in 2 patients. Two years after completed therapy PR was seen in 6 patients (33%), SD in additional 11 subjects (61%), single patient had PD. Clinical response to treatment in terms of PR and SD were noted in 22 patients (88%) after 1 year and in 14 patients (87%) after 2 years. Median PFS was 20 months, while the median OS was 23 months. In the 6 patients with clinical PD within initial 6 months the median PFS was 6 months and OS 11 months, while in those with SD or PR PFS was 22 months and OS 26 months (P<0.05). Conclusions: Therapy with ^{90}Y-DOTATATE} is effective in terms of clinical response, however the radiological response measured by the RECIST criteria underestimates benefits of this type of therapy in patients with progressive somatostatin receptor-positive midgut neuroendocrine carcinomas

Deep learning model for detection of pain intensity from facial expression

Many people who are suffering from a chronic pain face pe- riods of acute pain and resulting problems during their illness and ade- quate reporting of symptoms is necessary for treatment. Some patients have difficulties in adequately alerting caregivers to their pain or describ- ing the intensity which can impact on effective treatment. Pain and its intensity can be noticeable in ones face. Movements in facial muscles can depict ones current emotional state. Machine learning algorithms can detect pain intensity from facial expressions. The algorithm can ex- tract and classify facial expression of pain among patients. In this paper, we propose a new deep learning model for detection of pain intensity from facial expressions. This automatic pain detection system may help clinicians to detect pain and its intensity in patients and by doing this healthcare organizations may have access to more complete and more regular information of patients regarding their pain

Diagnostic imaging of neuroendocrine tumours

Guzy neuroendokrynne (NET) stanowią heterogenną grupę nowotworów posiadających mechanizmy gromadzenia prekursorów amin biogennych oraz ekspresję specyficznych białek receptorowych na swojej błonie komórkowej, które pomagają w lokalizacji oraz w leczeniu. Guzy typu NET różnią się między sobą substancjami wydzielanymi przez komórki, obecnością czynności hormonalnej lub jej brakiem, objawami klinicznymi, cechami histopatologicznymi oraz rokowaniem. Pochodzą one z gruczołów wydzielania wewnętrznego (przysadka, przytarczyce, rdzeń nadnercza) oraz dodatkowo z komórek rozlanego systemu endokrynnego o lokalizacji w ścianie przewodu pokarmowego, trzustce, tarczycy, grasicy czy w oskrzelach. Guzy NET o pochodzeniu GEP-NET stanowią większość tego typu guzów (> 70% wszystkich NET). Badania obrazowe i ocena swoistych markerów guzów NET umożliwiają identyfikację i ocenę stadium zaawansowania tych rzadkich nowotworów, a ponadto mają wartość prognostyczną. Najbardziej uniwersalną techniką obrazową wykorzystywaną w badaniu NET jest SRS. Innych badań obrazowych, takich jak spiralna wielorzędowa CT, MRI, endoskopowe oraz śródoperacyjne badanie USG, używa się do precyzyjnej anatomicznej lokalizacji zmian patologicznych. Kolejnym badaniem czynnościowym wykorzystywanym w diagnostyce NET jest scyntygrafia MIBG (metajodobenzyloguanidyna). Badanie to pozwala identyfikować przede wszystkim guz chromochłonny oraz MTC. W postaciach złośliwych guza chromochłonnego oraz raka rdzeniastego tarczycy znaczenie ma również badanie SRS. Bardzo ważnym aspektem wykorzystania technik obrazowych (zwłaszcza CT, SRS i MRI) jest ocena odpowiedzi na leczenie. Coraz większe znaczenie kliniczne w lokalizacji guzów NET ma badanie PET z użyciem nowych ligandów receptorowych wyznakowanych 68Ga. Wykorzystanie standardowego FDG PET stosuje się w guzach NET o wysokiej złośliwości.Neuroendocrine tumours (NET) consists of a heterogeneous group of neoplasms, that are able to express cell membrane neuroamine uptake mechanisms and/or specific receptors, such as somatostatin receptors, which can be used in the localization and treatment of these tumours. Conventionally NETs may present with a wide variety of functional or nonfunctional endocrine syndromes and may be familial and have other associated tumors, also they have different histology pattern and prognosis. They originate from endocrine glands such as the pituitary, the parathyroids, and the (neuroendocrine) adrenal, as well as endocrine islets within glandular tissue (thyroid or pancreatic) and cells dispersed between exocrine cells, such as endocrine cells of the digestive system (gastroenteropancreatic GEP-NET) and respiratory tracts. GEP-NET are the most common including more then 70% of all NETs.Imaging modalities and assessment of specific tumor markers offers high sensitivity in establishing the diagnosis and can also have prognostic significance. Most important single imaging technique in terms of initial identification and staging of GEP-NET seems to be somatostatin receptor scintigraphy (SRS). Other investigations like helical computed tomography (CT), magnetic resonance imaging (MRI), endoscopic and/or peri-operative ultrasonography are used for the precise localization of NET. Another one functional approach include MIBG (meta-iodobenzylguanidine scintigraphy). This technique is sensitive in the identification of chromaffin cell tumours pheochromocytoma, and also medullary thyroid carcinoma (MTC), although SRS seems to be very useful in the localization of malignant chromaffin cell tumours and MTC as well. The further localization and monitoring of the response to treatment CT, SRS and MRI are used with high diagnostic accuracy. More recently, positron emission tomography (PET) scanning is being increasingly used for the localization of NETs, due to develop new PET tracers (68Ga), the standard one FDG PET is currently used in groups of high malignant NET

SEWA DB: A rich database for audio-visual emotion and sentiment research in the wild

Natural human-computer interaction and audio-visual human behaviour sensing systems, which would achieve robust performance in-the-wild are more needed than ever as digital devices are becoming indispensable part of our life more and more. Accurately annotated real-world data are the crux in devising such systems. However, existing databases usually consider controlled settings, low demographic variability, and a single task. In this paper, we introduce the SEWA database of more than 2000 minutes of audio-visual data of 398 people coming from six cultures, 50% female, and uniformly spanning the age range of 18 to 65 years old. Subjects were recorded in two different contexts: while watching adverts and while discussing adverts in a video chat. The database includes rich annotations of the recordings in terms of facial landmarks, facial action units (FAU), various vocalisations, mirroring, and continuously valued valence, arousal, liking, agreement, and prototypic examples of (dis)liking. This database aims to be an extremely valuable resource for researchers in affective computing and automatic human sensing and is expected to push forward the research in human behaviour analysis, including cultural studies. Along with the database, we provide extensive baseline experiments for automatic FAU detection and automatic valence, arousal and (dis)liking intensity estimation

Comparing methods for assessment of facial dynamics in patients with major neurocognitive disorders

International audienceAssessing facial dynamics in patients with major neurocogni-tive disorders and specifically with Alzheimers disease (AD) has shown to be highly challenging. Classically such assessment is performed by clinical staff, evaluating verbal and non-verbal language of AD-patients, since they have lost a substantial amount of their cognitive capacity, and hence communication ability. In addition, patients need to communicate important messages, such as discomfort or pain. Automated methods would support the current healthcare system by allowing for telemedicine, i.e., lesser costly and logistically inconvenient examination. In this work we compare methods for assessing facial dynamics such as talking, singing, neutral and smiling in AD-patients, captured during music mnemotherapy sessions. Specifically, we compare 3D Con-vNets, Very Deep Neural Network based Two-Stream ConvNets, as well as Improved Dense Trajectories. We have adapted these methods from prominent action recognition methods and our promising results suggest that the methods generalize well to the context of facial dynamics. The Two-Stream ConvNets in combination with ResNet-152 obtains the best performance on our dataset, capturing well even minor facial dynamics and has thus sparked high interest in the medical community

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\u27s 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 \u3c 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 \u3c 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 \u3c 0.001) and higher BG Glx in patients relative to controls (g = 0.28, p \u3c 0.001). Proportion of males was negatively associated with MFC glutamate (z = -0.02, p \u3c 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

How unstable? Volatility and the genuinely new parties in Eastern Europe

Measuring of party system stability in Eastern Europe during the first decade of democratic elections presents problems. The traditional quantitative measure - volatility - does not distinguish between the dynamics among incumbent parties and the rise of genuinely new ones. I propose a new additional measure - success of genuinely new parties - and compare it to volatility. The subsequent performance of initially successful genuinely new parties is analysed. While volatility has been remarkably high in East European countries, the genuinely new parties have, in general, not been very successful. Instability of party systems in the region stems rather from the inner dynamics of incumbent actors than from the rise of new contenders