Ophthalmology

Accelerated Biological Aging in Exfoliation Glaucoma Assessed by Fundus-Derived Predicted Age and Advanced Glycation End Products.

Glaucoma is an age-related neurodegenerative disease characterized by progressive optic nerve damage. Accelerated biological aging, assessed using predicted age derived from fundus images, may serve as a biomarker for glaucoma progression. This study aimed to examine fundus-derived age acceleration among patients with primary open-angle glaucoma (POAG), exfoliation glaucoma (EXG), and controls, an...

Leveraging Vision Transformers in Multimodal Models for Retinal OCT Analysis.

Optical Coherence Tomography (OCT) has become an indispensable imaging modality in ophthalmology, providing high-resolution cross-sectional images of the retina. Accurate classification of OCT images is crucial for diagnosing retinal diseases such as Age-related Macular Degeneration (AMD) and Diabetic Macular Edema (DME). This study explores the efficacy of various deep learning models, including ...

Machine Learning-Enhanced Dual-Band Plasmonic Sensing for Simultaneous Qualitative and Quantitative Detection of Biomolecules in the Mid-Infrared Region.

Recently, sensing for biomolecules has become increasingly popular in the fields of environmental monitoring, personal health, and food safety. Plasmo...

Enhancing medical explainability in deep learning for age-related macular degeneration diagnosis.

Deep learning models hold significant promise for disease diagnosis but often lack transparency in their decision-making processes, limiting trust and...

Using explainable machine learning to predict the irritation and corrosivity of chemicals on eyes and skin.

Contact with specific chemicals often results in corrosive and irritative responses in the eyes and skin, playing a pivotal role in assessing the pote...

Quality prediction of seabream Sparus aurata by deep learning algorithms and explainable artificial intelligence.

In this study, Convolutional Neural Network (CNN), DenseNet121, Inception V3 and ResNet50 machine learning algorithms were used to determine the quali...

A 2D Semantic-Aware Position Encoding for Vision Transformers

Vision transformers have demonstrated significant advantages in computer vision tasks due to their ability to capture long-range dependencies and co...

FaceShield: Explainable Face Anti-Spoofing with Multimodal Large Language Models

Face anti-spoofing (FAS) is crucial for protecting facial recognition systems from presentation attacks. Previous methods approached this task as a ...

MAKE: Multi-Aspect Knowledge-Enhanced Vision-Language Pretraining for Zero-shot Dermatological Assessment

Dermatological diagnosis represents a complex multimodal challenge that requires integrating visual features with specialized clinical knowledge. Wh...

BioVFM-21M: Benchmarking and Scaling Self-Supervised Vision Foundation Models for Biomedical Image Analysis

Scaling up model and data size have demonstrated impressive performance improvement over a wide range of tasks. Despite extensive studies on scaling...

MetaUAS: Universal Anomaly Segmentation with One-Prompt Meta-Learning

Zero- and few-shot visual anomaly segmentation relies on powerful vision-language models that detect unseen anomalies using manually designed textua...

A vision transformer based CNN for underwater image enhancement ViTClarityNet.

Underwater computer vision faces significant challenges from light scattering, absorption, and poor illumination, which severely impact underwater vis...

Development and validation of a deep learning model for diagnosing neuropathic corneal pain via in vivo confocal microscopy.

Neuropathic corneal pain (NCP) is an underdiagnosed ocular disorder caused by aberrant nociception and hypersensitivity of corneal nerves, often resul...

Evaluation of data collection and annotation approaches of driver gaze dataset.

Driver gaze estimation is important for various driver gaze applications such as building advanced driving assistance systems and understanding driver...

[Radiosurgery of benign intracranial lesions. Indications, results , and perspectives].

Stereotactic radiosurgery (SRS) is a non-invasive technique that is transforming the management of benign intracranial lesions through its precision a...

Prioritizing Image-Related Tokens Enhances Vision-Language Pre-Training

In standard large vision-language models (LVLMs) pre-training, the model typically maximizes the joint probability of the caption conditioned on the...

OpenThinkIMG: Learning to Think with Images via Visual Tool Reinforcement Learning

While humans can flexibly leverage interactive visual cognition for complex problem-solving, enabling Large Vision-Language Models (LVLMs) to learn ...

A portable diagnosis model for Keratoconus using a smartphone

Keratoconus (KC) is a corneal disorder that results in blurry and distorted vision. Traditional diagnostic tools, while effective, are often bulky, ...

Symbolically-Guided Visual Plan Inference from Uncurated Video Data

Visual planning, by offering a sequence of intermediate visual subgoals to a goal-conditioned low-level policy, achieves promising performance on lo...

DHECA-SuperGaze: Dual Head-Eye Cross-Attention and Super-Resolution for Unconstrained Gaze Estimation

Unconstrained gaze estimation is the process of determining where a subject is directing their visual attention in uncontrolled environments. Gaze e...