Ophthalmology

TU_MyCo-Vision: A Deep Learning Tool for Detection of Cell Morphologies in Fungal Microscopic Images

Morphological switching in response to environmental stimuli is a well-known phenomenon in fungi, leading to diverse morphotypes. Microscopic observation remains a widely used approach to study these phenotypes. However, variation in sample preparation and operators skill can limit the scale of sample processing or introduce bias. Although several image-based cell detection tools have been develop...

Functional immune state classification of unlabeled live human monocytes using holotomography and machine learning

Precise evaluation of immune status is critical for managing diseases such as sepsis, in which the immune system transitions between hyper-inflammatory and immune-suppressed states. However, current biomarkers are limited by low specificity and time-consuming protocols. Here, we present a label-free, imaging-based framework for single-cell immune profiling of human monocytes using three-dimensiona...

Intra-DNA k-mer Conservation Patterns Encode Evolutionary Selection of Variants

Evolution shapes the structure and content of genomes, yet the contribution of local sequence composition to variant selection remains poorly understo...

Emergent Neuronal Mechanisms Mediating Covert Attention in Convolutional Neural Networks

Covert visual attention allows the brain to select different regions of the visual world without eye movements. Predictive cues of a target location o...

Compatibility of a competition model for explaining eye fixation durations during free viewing

Intersaccadic times or eye fixation durations (EFD) are relatively stable at around 250ms, equivalent to 4 saccades by second. However, the mean and s...

Dental Age Assessment of White-tailed Deer via Computer Vision and Deep Learning

Accurate age estimation of white-tailed deer remains challenging for wildlife management, with existing computer vision methods limited to trail camer...

From manual counting to YOLO: Using computer vision to automate large-scale fecundity assays in C. elegans

Fecundity measurements play a crucial role in life history research, providing insights into reproductive fitness, population dynamics, and environmen...

Single Capture Quantitative Oblique Back-Illumination Microscopy

Quantitative oblique back-illumination microscopy (qOBM) has emerged as a powerful technique for label-free, 3D quantitative phase imaging of arbitrar...

Comparing Robotic and Computer Vision Assessments of Unilateral and Bilateral Reaching in Healthy Adults

Assessment of reaching is foundational to upper limb neurorehabilitation. Current neurorehabilitation needs have increased the demand for quantitative...

CV.eDNA: A hybrid approach to invertebrate biomonitoring using computer vision and DNA metabarcoding

Automated invertebrate classification using computer vision has shown significant potential to improve specimen processing efficiency. However, challe...

CVAE-based Causal Representation Learning from Retinal Fundus Images for Age Related Macular Degeneration(AMD) Prediction

Regarding relatively poor prognosis and acute vision impairment, analyzing Age-Related Macular Degeneration, or AMD has been one of the most important...

Interpreting convolutional neural networks to study wide-field amacrine cell inhibition in the retina

Wide-field amacrine cells (ACs) play a unique role in retinal processing by integrating visual information across a large spatial area. Their inhibito...

Digit-Tracking Reveals Curiosity-Driven Visual Attention in Macaque Monkeys

We used digit-tracking, a touch-based method for assessing visual attention, to investigate spontaneous exploration in macaque monkeys. By engaging wi...

Machine Learning-Driven Prediction of TLR4 Binding Affinity: A Comprehensive Molecular Feature Analysis for Drug Discovery

Toll-like receptor 4 (TLR4) represents a promising therapeutic target for inflammatory diseases and cancer, but developing selective modulators remain...

A lightweight, physics-based, sensor-fusion filter for real-time EEG denoising and improved downstream AI classification

Physiological time-series data, like electroencephalography (EEG), are vulnerable to motion, ocular, and muscle artifacts that hinder real-time infere...

A systematic comparison of predictive models on the retina

Understanding the nonlinear encoding mechanisms of retinal ganglion cells (RGCs) in response to various visual stimuli presents a central challenge in...

A Reinforcement Learning and Sequential Sampling Model Constrained by Gaze Data

Reinforcement learning models can be combined with sequential sampling models to fit choice-RT data. The combined models, known as RL-SSMs, explain a ...

Shared texture-like representations, not global form, underlie deep neural network alignment with human visual processing

Deep neural networks (DNNs) are a leading computational framework for understanding neural visual processing. A standard approach for evaluating their...

Neural network-based encoding in free-viewing fMRI with gaze-aware models

Representations learned by convolutional neural networks (CNNs) exhibit a remarkable resemblance to information processing patterns observed in the pr...

Learning Residual-based Biomarkers of Cognitive Health via Self-Supervised Learning on EEG State Transitions

Deep learning (DL) models have achieved impressive performance in EEG-based prediction tasks, but they often lack interpretability, limiting their cli...