automatic

Speech-driven gesture generation is an emerging field within virtual human creation. However, a significant challenge lies in accurately determining and processing the multitude of input features (such as acoustic, semantic, emotional, personality, and even subtle unknown features). Traditional approaches, reliant on various explicit feature inputs and complex multimodal processing, constrain the expressiveness of resulting gestures and limit their applicability. To address these challenges, we present Persona-Gestor, a novel end-to-end generative model designed to generate highly personalized 3D full-body gestures solely relying on raw speech audio...

Generating radiology reports automatically reduces the workload of radiologists and helps the diagnoses of specific diseases. Many existing methods take this task as modality transfer process. However, since the key information related to disease accounts for a small proportion in both image and report, it is hard for the model to learn the latent relation between the radiology image and its report, thus failing to generate fluent and accurate radiology reports. To tackle this problem, we propose a memory-based cross-modal semantic alignment model (MCSAM) following an encoder-decoder paradigm...

In this paper, we propose a graph-represented image distribution similarity (GRIDS) index for full-reference (FR) image quality assessment (IQA), which can measure the perceptual distance between distorted and reference images by assessing the disparities between their distribution patterns under a graph-based representation. First, we transform the input image into a graph-based representation, which is proven to be a versatile and effective choice for capturing visual perception features. This is achieved through the automatic generation of a vision graph from the given image content, leading to holistic perceptual associations for irregular image regions...

Automated cardiac segmentation from two-dimensional (2D) echocardiographic images is a crucial step toward improving clinical diagnosis. Anatomical heterogeneity and inherent noise, however, present technical challenges and lower segmentation accuracy. The objective of this study is to propose a method for the automatic segmentation of the ventricular endocardium, the myocardium, and the left atrium, in order to accurately determine clinical indices. Specifically, we suggest using the recently introduced pixel-to-pixel Generative Adversarial Network (Pix2Pix GAN) model for accurate segmentation...

Cervical cancer is a common cancer in women globally, with treatment usually involving radiation therapy (RT). Accurate segmentation for the tumour site and organ-at-risks (OARs) could assist in the reduction of treatment side effects and improve treatment planning efficiency. Cervical cancer Magnetic Resonance Imaging (MRI) segmentation is challenging due to a limited amount of training data available and large inter- and intra- patient shape variation for OARs. The proposed Masked-Net consists of a masked encoder within the 3D U-Net to account for the large shape variation within the dataset, with additional dilated layers added to improve segmentation performance...

BACKGROUND: Current evidence of volume changes in hippocampal subdivisions in schizophrenia remains inconsistent, and few studies have investigated the relationship between regional hippocampal volumes and symptom remission. METHODS: In this cross-sectional study, we recruited 31 patients with schizophrenia and 31 healthy controls (HCs). Symptomatic remission in schizophrenia was determined according to Remission in Schizophrenia Working Group criteria. The volumes of hippocampal longitudinal subregions and transverse subfields were measured using manual and automatic techniques, respectively...

OBJECTIVES: Assessment of plaque stenosis severity allows better management of carotid source of stroke. Our objective is to create a deep learning (DL) model to segment carotid intima-media thickness and plaque and further automatically calculate plaque stenosis severity on common carotid artery (CCA) transverse section ultrasound images. METHODS: Three hundred and ninety images from 376 individuals were used to train (235/390, 60%), validate (39/390, 10%), and test (116/390, 30%) on a newly proposed CANet model...

BACKGROUND: MRI-based placental analyses have been used to improve fetal growth restriction (FGR) assessment by complementing ultrasound-based measurements. However, these are still limited by time-consuming manual annotation in MRI data and the lack of mother-based information. PURPOSE: To develop and validate a hybrid model for accurate FGR assessment by automatic placental radiomics on T2-weighted imaging (T2WI) and multifeature fusion. STUDY TYPE: Retrospective...

BACKGROUND: CD8+ tumor-infiltrating lymphocyte (TIL) predicts response to anti-PD-(L)1 therapy. However, there remains no standardized method to assess CD8+ TIL in melanoma, and developing a specific, cost-effective, reproducible, and clinically actionable biomarker to anti-PD-(L)1 remains elusive. We report on the development of automatic CD8+ TIL density quantification via whole slide image (WSI) analysis in advanced melanoma patients treated with front-line anti-PD-1 blockade, and correlation immunotherapy response...

Bullying victimization is widely accepted to be associated with aggression. However, the mechanisms underlying this relationship remain unknown. To examine the long-term impact of bullying victimization on aggression, the present study tested the potential mediating effects of both anger rumination and hostile automatic thoughts. A total of 809 undergraduates from four universities across China (74.80% female; Mage  = 19.63 years, SD = 0.82 years) completed the survey on three occasions, with a 6-month delay between Time 1 and Time 2 and a 1-year interval between Time 2 and Time 3...

OBJECTIVE: This study aimed to develop an automated detection schema for otosclerosis with interpretable deep learning using temporal bone computed tomography images. METHODS: With approval from the institutional review board, we retrospectively analyzed high-resolution computed tomography scans of the temporal bone of 182 participants with otosclerosis (67 male subjects and 115 female subjects; average age, 36.42 years) and 157 participants without otosclerosis (52 male subjects and 102 female subjects; average age, 30...

-The automatic identification of human physical activities, commonly referred to as Human Activity Recognition (HAR), has garnered significant interest and application across various sectors, including entertainment, sports, and notably health. Within the realm of health, a myriad of applications exists, contingent upon the nature of experimentation, the activities under scrutiny, and the methodology employed for data and information acquisition. This diversity opens doors to multifaceted applications, including support for the well-being and safeguarding of elderly individuals afflicted with neurodegenerative diseases, especially in the context of smart homes...

BACKGROUND: Fruit freshness detection by computer vision is essential for many agricultural applications, e.g., automatic harvesting and supply chain monitoring. This paper proposes to use the multi-task learning (MTL) paradigm to build a deep convolutional neural work for fruit freshness detection. RESULTS: We design an MTL model that optimizes the freshness detection (T1 ) and fruit type classification (T2 ) tasks in parallel. The model uses a shared CNN (convolutional neural network) subnet and two FC (fully connected) task heads...

Consensus criteria for traumatic encephalopathy syndrome (TES) specify that at least one core clinical feature of cognitive impairment (CI; e.g., difficulties with memory, executive function) or neurobehavioral dysregulation (ND; e.g., explosiveness, rage, and mood lability) be present and not fully accounted for by other health disorders. Associations between self-reported symptoms that mirror the core clinical features of TES-and how they may be related to concomitant medical conditions-remain unclear. The purpose of this study was to evaluate the association of medical conditions and football exposures with TES clinical features (CI+/- , ND+/- ) in 1741 former professional American-style football (ASF) players (age, 57...

[This corrects the article DOI: 10.3389/fnins.2022.996957.].

Introduction: Atrial fibrillation (AF) is the most common cardiac arrhythmia, which is clinically identified with irregular and rapid heartbeat rhythm. AF puts a patient at risk of forming blood clots, which can eventually lead to heart failure, stroke, or even sudden death. Electrocardiography (ECG), which involves acquiring bioelectrical signals from the body surface to reflect heart activity, is a standard procedure for detecting AF. However, the occurrence of AF is often intermittent, costing a significant amount of time and effort from medical doctors to identify AF episodes...

Background and study aims Capsule endoscopy (CE) is commonly used as the initial exam for suspected mid-gastrointestinal bleeding after normal upper and lower endoscopy. Although the assessment of the small bowel is the primary focus of CE, detecting upstream or downstream vascular lesions may also be clinically significant. This study aimed to develop and test a convolutional neural network (CNN)-based model for panendoscopic automatic detection of vascular lesions during CE. Patients and methods A multicentric AI model development study was based on 1022 CE exams...

The threat from novel marine species introductions is a global issue. When non-native marine species are introduced to novel environments and become invasive, they can affect biodiversity, industry, ecosystem function, and both human and wildlife health. Isolated areas with sensitive or highly specialised endemic species can be particularly impacted. The global increase in the scope of tourism and other human activities, together with a rapidly changing climate, now put these remote ecosystems under threat...

INTRODUCTION: The automatic segmentation approaches of rectal cancer from magnetic resonance imaging (MRI) are very valuable to relieve physicians from heavy workloads and enhance working efficiency. This study aimed to compare the segmentation accuracy of a proposed model with the other three models and the inter-observer consistency. METHODS: A total of 65 patients with rectal cancer who underwent MRI examination were enrolled in our cohort and were randomly divided into a training cohort (n = 45) and a validation cohort (n = 20)...

BACKGROUND: Allergic rhinitis constitutes a widespread health concern, with traditional treatments often proving to be painful and ineffective. Acupuncture targeting the pterygopalatine fossa proves effective but is complicated due to the intricate nearby anatomy. METHODS: To enhance the safety and precision in targeting the pterygopalatine fossa, we introduce a deep learning-based model to refine the segmentation of the pterygopalatine fossa. Our model expands the U-Net framework with DenseASPP and integrates an attention mechanism for enhanced precision in the localisation and segmentation of the pterygopalatine fossa...