The Motion Picture: Leveraging Movement to Enhance AI Object Detection in Ecology.

The rise of AI has seen an explosion in the use of deep learning methods that automate the analysis of image and video data, saving ecologists vast amounts of time and resources. Ecological imagery poses unique challenges; however, with cryptic species struggling to be detected among poor visibility and diverse environments. We propose leveraging movement information to attempt to improve the predictions produced by a high-performing object detection algorithm.

Dual-Path Adversarial Denoising Network Based on UNet.

Digital image quality is crucial for reliable analysis in applications such as medical imaging, satellite remote sensing, and video surveillance. However, traditional denoising methods struggle to balance noise removal with detail preservation and lack adaptability to various types of noise. We propose a novel three-module architecture for image denoising, comprising a generator, a dual-path-UNet-based denoiser, and a discriminator.

Automated instance segmentation and registration of spinal vertebrae from CT-Scans with an improved 3D U-net neural network and corner point registration.

This paper presents a rapid and robust approach for 3D volumetric segmentation, labelling, and registration of human spinal vertebrae from CT scans using an optimised and improved 3D U-Net neural network architecture. The network is designed by incorporating residual and dense interconnections, followed by an extensive evaluation of different network setups by optimising the network components like activation functions, optimisers, and pooling operations. In addition, the network architecture is optimised for varying numbers of convolution layers per block and U-Net levels with fixed and cascading numbers of filters.

A novel and effective strategy for the treatment of large hepatic hemangioma: combining preoperative embolization with laparoscopic-assisted and ultrasound-guided ablation.

Background: Hepatic hemangioma is the most common benign liver tumor. This study aims to evaluate the feasibility, safety and efficacy of Trans-arterial embolization (TAE) combined with thermal ablation in the treatment of large hepatic hemangioma (> 5 cm).

Methods: From January 2018 to December 2021, 82 patients and 112 large HH with a maximum mean diameter of 8.

Perioperative management of acute kidney injury after microwave ablation of hepatic hemangioma.

Objective: To improve treatment options for acute kidney injury (AKI) after microwave ablation (MWA) of hepatic hemangioma (HH).

Methods: From January 1, 2021, to October 28, 2024, 117 patients with HH were treated by MWA at our center, and 2 of them occurred AKI after operation. The preoperative and postoperative data of 2 patients were retrospectively analyzed.

Algae Biomass Hydrogels for Enhanced Removal of Heavy Metal Ions.

Heavy metal ion pollution in aquatic environments is a critical global issue, damaging ecosystems and threatening human health via bioaccumulation in the food chain. Despite promising progress with biosorbents, the development of environmentally friendly and stable heavy metal adsorbents requires further exploration. In this study, we present an algae-loaded alginate hydrogel as a composite adsorbent for heavy metals.

Improving traceability and quality control in the red-meat industry through computer vision-driven physical meat feature tracking.

Current traceability systems rely heavily on external markers which can be altered or tampered with. We hypothesized that the unique intramuscular fat patterns in beef cuts could serve as natural physical identifiers for traceability, while simultaneously providing information about quality attributes. To test our hypothesis, we developed a comprehensive dataset of 38,528 high-resolution beef images from 602 steaks with annotations from human grading and ingredient analysis.

NAT10 PROMOTES PYROPTOSIS AND PANCREATIC INJURY OF SEVERE ACUTE PANCREATITIS THROUGH AC4C MODIFICATION OF NLRP3.

Severe acute pancreatitis (SAP) is a highly morbid acute digestive disorder linked to pyroptosis. N-acetyltransferase 10 (NAT10) facilitates the production of N4-acetylcytidine (ac4C) modifications in mRNA, thereby contributing to the progression of various diseases. However, the specific role of NAT10 in SAP remains to be elucidated.

Attention-based multi-residual network for lung segmentation in diseased lungs with custom data augmentation.

Lung disease analysis in chest X-rays (CXR) using deep learning presents significant challenges due to the wide variation in lung appearance caused by disease progression and differing X-ray settings. While deep learning models have shown remarkable success in segmenting lungs from CXR images with normal or mildly abnormal findings, their performance declines when faced with complex structures, such as pulmonary opacifications. In this study, we propose AMRU++, an attention-based multi-residual UNet++ network designed for robust and accurate lung segmentation in CXR images with both normal and severe abnormalities.

AMFP-net: Adaptive multi-scale feature pyramid network for diagnosis of pneumoconiosis from chest X-ray images.

Early detection of pneumoconiosis by routine health screening of workers in the mining industry is critical for preventing the progression of this incurable disease. Automated pneumoconiosis classification in chest X-ray images is challenging due to the low contrast of opacities, inter-class similarity, intra-class variation and the existence of artifacts. Compared to traditional methods, convolutional neural networks have shown significant improvement in pneumoconiosis classification tasks, however, accurate classification remains challenging due to mainly the inability to focus on semantically meaningful lesion opacities.

DLA-Net: dual lesion attention network for classification of pneumoconiosis using chest X-ray images.

Accurate and early detection of pneumoconiosis using chest X-rays (CXR) is important for preventing the progression of this incurable disease. It is also a challenging task due to large variations in appearance, size and location of lesions in the lung regions as well as inter-class similarity and intra-class variance. Compared to traditional methods, Convolutional Neural Networks-based methods have shown improved results; however, these methods are still not applicable in clinical practice due to limited performance.

Risk factors and cognitive correlates of white matter hyperintensities in ethnically diverse populations without dementia: The COSMIC consortium.

Introduction: White matter hyperintensities (WMHs) are an important imaging marker for cerebral small vessel diseases, but their risk factors and cognitive associations have not been well documented in populations of different ethnicities and/or from different geographical regions.

Methods: We investigated how WMHs were associated with vascular risk factors and cognition in both Whites and Asians, using data from five population-based cohorts of non-demented older individuals from Australia, Singapore, South Korea, and Sweden ( = 1946). WMH volumes (whole brain, periventricular, and deep) were quantified with UBO Detector and harmonized using the ComBat model.

3 directional Inception-ResUNet: Deep spatial feature learning for multichannel singing voice separation with distortion.

Singing voice separation on robots faces the problem of interpreting ambiguous auditory signals. The acoustic signal, which the humanoid robot perceives through its onboard microphones, is a mixture of singing voice, music, and noise, with distortion, attenuation, and reverberation. In this paper, we used the 3D Inception-ResUNet structure in the U-shaped encoding and decoding network to improve the utilization of the spatial and spectral information of the spectrogram.

Risk factors and cognitive correlates of white matter hyperintensities in ethnically diverse populations without dementia: the COSMIC consortium.

Introduction: White matter hyperintensities (WMH) are an important imaging marker for cerebral small vessel diseases, but their risk factors and cognitive associations have not been well-documented in populations of different ethnicities and/or from different geographical regions.

Method: Magnetic resonance imaging data of five population-based cohorts of non-demented older individuals from Australia, Singapore, South Korea, and Sweden (N = 1,946) were examined for WMH and their associations with vascular risk factors and cognition.

Result: Factors associated with larger whole brain WMH volumes included diabetes, hypertension, stroke, current smoking, body mass index, higher alcohol intake and insufficient physical activity.

Ordered growth of metal oxides in patterned multi-angle microstructures.

Herein, we report a facile method combining top-down patterning transfer and bottom-up nanorod growth for preparing large-area and ordered TiO nanorod arrays. Pre-crystallization seeding was patterned with nanostructured morphologies interfacial tension-driven precursor solution scattering on various types and period templates. This is a widely applicable strategy for capillary force-driven interfacial patterns, which also shows great operability in complex substrate morphologies with multiple-angle mixing.

A Multi-Scale Context Aware Attention Model for Medical Image Segmentation.

Medical image segmentation is critical for efficient diagnosis of diseases and treatment planning. In recent years, convolutional neural networks (CNN)-based methods, particularly U-Net and its variants, have achieved remarkable results on medical image segmentation tasks. However, they do not always work consistently on images with complex structures and large variations in regions of interest (ROI).

Monitoring of Pigmented Skin Lesions Using 3D Whole Body Imaging.

Background And Objectives: Advanced artificial intelligence and machine learning have great potential to redefine how skin lesions are detected, mapped, tracked and documented. Here, we propose a 3D whole-body imaging system known as 3DSkin-mapper to enable automated detection, evaluation and mapping of skin lesions.

Methods: A modular camera rig arranged in a cylindrical configuration was designed to automatically capture images of the entire skin surface of a subject synchronously from multiple angles.

Space-Confined seeding and growth of ordered arrays of TiO hierarchical nanostructures.

Here, we report a facile approach to fabricate large area ordered arrays of TiO hierarchical nanostructures through space-confined seeding and growth on inverted pyramid templates. The mechanisms of space-confined seeding and growth have been systematically explored and studied. The drying TiO seed precursor solution prefers to accumulate on the narrow structures including the centre and edges of the inverted pyramid structures, which facilitates to reduce the free energy of the precursor solution surface and form crystal seeds.

Deep Ensemble Learning for the Automatic Detection of Pneumoconiosis in Coal Worker's Chest X-ray Radiography.

Globally, coal remains one of the natural resources that provide power to the world. Thousands of people are involved in coal collection, processing, and transportation. Particulate coal dust is produced during these processes, which can crush the lung structure of workers and cause pneumoconiosis.

Detection and Visualisation of Pneumoconiosis Using an Ensemble of Multi-Dimensional Deep Features Learned from Chest X-rays.

Pneumoconiosis is a group of occupational lung diseases induced by mineral dust inhalation and subsequent lung tissue reactions. It can eventually cause irreparable lung damage, as well as gradual and permanent physical impairments. It has affected millions of workers in hazardous industries throughout the world, and it is a leading cause of occupational death.

Computer-aided extraction of select MRI markers of cerebral small vessel disease: A systematic review.

Cerebral small vessel disease (CSVD) is a major vascular contributor to cognitive impairment in ageing, including dementias. Imaging remains the most promising method for in vivo studies of CSVD. To replace the subjective and laborious visual rating approaches, emerging studies have applied state-of-the-art artificial intelligence to extract imaging biomarkers of CSVD from MRI scans.

Computer-Aided Diagnosis of Coal Workers' Pneumoconiosis in Chest X-ray Radiographs Using Machine Learning: A Systematic Literature Review.

Computer-aided diagnostic (CAD) systems can assist radiologists in detecting coal workers' pneumoconiosis (CWP) in their chest X-rays. Early diagnosis of the CWP can significantly improve workers' survival rate. The development of the CAD systems will reduce risk in the workplace and improve the quality of chest screening for CWP diseases.

Weakly Supervised RGB-D Salient Object Detection With Prediction Consistency Training and Active Scribble Boosting.

RGB-D salient object detection (SOD) has attracted increasingly more attention as it shows more robust results in complex scenes compared with RGB SOD. However, state-of-the-art RGB-D SOD approaches heavily rely on a large amount of pixel-wise annotated data for training. Such densely labeled annotations are often labor-intensive and costly.

Deep Learning for Object Detection, Classification and Tracking in Industry Applications.

Object detection, classification and tracking are three important computer vision techniques [...

Automatic detection of fish and tracking of movement for ecology.

Animal movement studies are conducted to monitor ecosystem health, understand ecological dynamics, and address management and conservation questions. In marine environments, traditional sampling and monitoring methods to measure animal movement are invasive, labor intensive, costly, and limited in the number of individuals that can be feasibly tracked. Automated detection and tracking of small-scale movements of many animals through cameras are possible but are largely untested in field conditions, hampering applications to ecological questions.