Ophthalmology

Hybrid convolutional neural network optimized with an artificial algae algorithm for glaucoma screening using fundus images.

OBJECTIVE: We developed an optimized decision support system for retinal fundus image-based glaucoma screening.

A Computational Framework for Modeling Emergence of Color Vision in the Human Brain

It is a mystery how the brain decodes color vision purely from the optic nerve signals it receives, with a core inferential challenge being how it disentangles internal perception with the correct color dimensionality from the unknown encoding properties of the eye. In this paper, we introduce a computational framework for modeling this emergence of human color vision by simulating both the eye ...

Identifying Terrain Physical Parameters from Vision -- Towards Physical-Parameter-Aware Locomotion and Navigation

Identifying the physical properties of the surrounding environment is essential for robotic locomotion and navigation to deal with non-geometric haz...

Universal dimensions of visual representation

Do neural network models of vision learn brain-aligned representations because they share architectural constraints and task objectives with biologi...

Smartphone-based Eye Tracking System using Edge Intelligence and Model Optimisation

A significant limitation of current smartphone-based eye-tracking algorithms is their low accuracy when applied to video-type visual stimuli, as the...

Comparative Analysis of Macular and Optic Disc Perfusion Pre and Post Silicone Oil Removal: A Machine Learning Approach.

In the realm of ophthalmic surgeries, silicone oil is often utilized as a tamponade agent for repairing retinal detachments, but it necessitates subse...

Lookism: The overlooked bias in computer vision

In recent years, there have been significant advancements in computer vision which have led to the widespread deployment of image recognition and ge...

Kalib: Easy Hand-Eye Calibration with Reference Point Tracking

Hand-eye calibration aims to estimate the transformation between a camera and a robot. Traditional methods rely on fiducial markers, which require c...

Human Eyes-Inspired Recurrent Neural Networks Are More Robust Against Adversarial Noises.

Humans actively observe the visual surroundings by focusing on salient objects and ignoring trivial details. However, computer vision models based on ...

Polaris: Open-ended Interactive Robotic Manipulation via Syn2Real Visual Grounding and Large Language Models

This paper investigates the task of the open-ended interactive robotic manipulation on table-top scenarios. While recent Large Language Models (LLMs...

Surgical-VQLA++: Adversarial Contrastive Learning for Calibrated Robust Visual Question-Localized Answering in Robotic Surgery

Medical visual question answering (VQA) bridges the gap between visual information and clinical decision-making, enabling doctors to extract underst...

Connective Viewpoints of Signal-to-Noise Diffusion Models

Diffusion models (DM) have become fundamental components of generative models, excelling across various domains such as image creation, audio genera...

Empirical Analysis of Large Vision-Language Models against Goal Hijacking via Visual Prompt Injection

We explore visual prompt injection (VPI) that maliciously exploits the ability of large vision-language models (LVLMs) to follow instructions drawn ...

Lifelong Personalized Low-Rank Adaptation of Large Language Models for Recommendation

We primarily focus on the field of large language models (LLMs) for recommendation, which has been actively explored recently and poses a significan...

VisionUnite: A Vision-Language Foundation Model for Ophthalmology Enhanced with Clinical Knowledge

The need for improved diagnostic methods in ophthalmology is acute, especially in the less developed regions with limited access to specialists and ...

Explainable Emotion Decoding for Human and Computer Vision

Modern Machine Learning (ML) has significantly advanced various research fields, but the opaque nature of ML models hinders their adoption in severa...

DEEP LEARNING FOR AUTOMATIC PREDICTION OF EARLY ACTIVATION OF TREATMENT-NAIVE NONEXUDATIVE MACULAR NEOVASCULARIZATIONS IN AGE-RELATED MACULAR DEGENERATION.

BACKGROUND: Around 30% of nonexudative macular neovascularizations exudate within 2 years from diagnosis in patients with age-related macular degenera...

Predicting intraocular lens tilt using a machine learning concept.

PURPOSE: To use a combination of partial least squares regression and a machine learning approach to predict intraocular lens (IOL) tilt using preoper...

Machine Learning Models for Predicting Cycloplegic Refractive Error and Myopia Status Based on Non-Cycloplegic Data in Chinese Students.

PURPOSE: To develop and validate machine learning (ML) models for predicting cycloplegic refractive error and myopia status using noncycloplegic refra...

Deep Learning-Based Longitudinal Prediction of Childhood Myopia Progression Using Fundus Image Sequences and Baseline Refraction Data

Childhood myopia constitutes a significant global health concern. It exhibits an escalating prevalence and has the potential to evolve into severe, ...