Ophthalmology

Exploring Kernel Transformations for Implicit Neural Representations

Implicit neural representations (INRs), which leverage neural networks to represent signals by mapping coordinates to their corresponding attributes, have garnered significant attention. They are extensively utilized for image representation, with pixel coordinates as input and pixel values as output. In contrast to prior works focusing on investigating the effect of the model's inside component...

Opening the black box of deep learning: Validating the statistical association between explainable artificial intelligence (XAI) and clinical domain knowledge in fundus image-based glaucoma diagnosis

While deep learning has exhibited remarkable predictive capabilities in various medical image tasks, its inherent black-box nature has hindered its widespread implementation in real-world healthcare settings. Our objective is to unveil the decision-making processes of deep learning models in the context of glaucoma classification by employing several Class Activation Map (CAM) techniques to gene...

Resilience of Vision Transformers for Domain Generalisation in the Presence of Out-of-Distribution Noisy Images

Modern AI models excel in controlled settings but often fail in real-world scenarios where data distributions shift unpredictably - a challenge know...

Artificial intelligence application in lymphoma diagnosis: from Convolutional Neural Network to Vision Transformer

Recently, vision transformers were shown to be capable of outperforming convolutional neural networks when pretrained on sufficiently large datasets...

Deep Learning-Enhanced Robotic Subretinal Injection with Real-Time Retinal Motion Compensation

Subretinal injection is a critical procedure for delivering therapeutic agents to treat retinal diseases such as age-related macular degeneration (A...

Semantic-guided Representation Learning for Multi-Label Recognition

Multi-label Recognition (MLR) involves assigning multiple labels to each data instance in an image, offering advantages over single-label classifica...

Early detection of diabetes through transfer learning-based eye (vision) screening and improvement of machine learning model performance and advanced parameter setting algorithms

Diabetic Retinopathy (DR) is a serious and common complication of diabetes, caused by prolonged high blood sugar levels that damage the small retina...

MultiClear: Multimodal Soft Exoskeleton Glove for Transparent Object Grasping Assistance

Grasping is a fundamental skill for interacting with the environment. However, this ability can be difficult for some (e.g. due to disability). Wear...

Mamba as a Bridge: Where Vision Foundation Models Meet Vision Language Models for Domain-Generalized Semantic Segmentation

Vision Foundation Models (VFMs) and Vision-Language Models (VLMs) have gained traction in Domain Generalized Semantic Segmentation (DGSS) due to the...

TokenFLEX: Unified VLM Training for Flexible Visual Tokens Inference

Conventional Vision-Language Models(VLMs) typically utilize a fixed number of vision tokens, regardless of task complexity. This one-size-fits-all s...

Symbiotic AI: Augmenting Human Cognition from PCs to Cars

As AI takes on increasingly complex roles in human-computer interaction, fundamental questions arise: how can HCI help maintain the user as the prim...

QID: Efficient Query-Informed ViTs in Data-Scarce Regimes for OCR-free Visual Document Understanding

In Visual Document Understanding (VDU) tasks, fine-tuning a pre-trained Vision-Language Model (VLM) with new datasets often falls short in optimizin...

STING-BEE: Towards Vision-Language Model for Real-World X-ray Baggage Security Inspection

Advancements in Computer-Aided Screening (CAS) systems are essential for improving the detection of security threats in X-ray baggage scans. However...

HQViT: Hybrid Quantum Vision Transformer for Image Classification

Transformer-based architectures have revolutionized the landscape of deep learning. In computer vision domain, Vision Transformer demonstrates remar...

A Sensorimotor Vision Transformer

This paper presents the Sensorimotor Transformer (SMT), a vision model inspired by human saccadic eye movements that prioritize high-saliency region...

Re-thinking Temporal Search for Long-Form Video Understanding

Efficiently understanding long-form videos remains a significant challenge in computer vision. In this work, we revisit temporal search paradigms fo...

Image Coding for Machines via Feature-Preserving Rate-Distortion Optimization

Many images and videos are primarily processed by computer vision algorithms, involving only occasional human inspection. When this content requires...

Multimodal Reference Visual Grounding

Visual grounding focuses on detecting objects from images based on language expressions. Recent Large Vision-Language Models (LVLMs) have significan...

Towards Unified Referring Expression Segmentation Across Omni-Level Visual Target Granularities

Referring expression segmentation (RES) aims at segmenting the entities' masks that match the descriptive language expression. While traditional RES...

Image Difference Grounding with Natural Language

Visual grounding (VG) typically focuses on locating regions of interest within an image using natural language, and most existing VG methods are lim...