Ophthalmology

Analysis of human visual field information using machine learning methods and assessment of their accuracy

Subject of research: is the study of methods for analyzing perimetric images for the diagnosis and control of glaucoma diseases. Objects of research: is a dataset collected on the ophthalmological perimeter with the results of various patient pathologies, since the ophthalmological community is acutely aware of the issue of disease control and import substitution. [5]. Purpose of research: is to...

AI-Driven Diabetic Retinopathy Diagnosis Enhancement through Image Processing and Salp Swarm Algorithm-Optimized Ensemble Network

Diabetic retinopathy is a leading cause of blindness in diabetic patients and early detection plays a crucial role in preventing vision loss. Traditional diagnostic methods are often time-consuming and prone to errors. The emergence of deep learning techniques has provided innovative solutions to improve diagnostic efficiency. However, single deep learning models frequently face issues related t...

CoSpace: Benchmarking Continuous Space Perception Ability for Vision-Language Models

Vision-Language Models (VLMs) have recently witnessed significant progress in visual comprehension. As the permitting length of image context grows,...

Streamlining SIMD ISA Extensions with Takum Arithmetic: A Case Study on Intel AVX10.2

Modern microprocessors extend their instruction set architecture (ISA) with Single Instruction, Multiple Data (SIMD) operations to improve performan...

Foundation Feature-Driven Online End-Effector Pose Estimation: A Marker-Free and Learning-Free Approach

Accurate transformation estimation between camera space and robot space is essential. Traditional methods using markers for hand-eye calibration req...

Improving LLM Video Understanding with 16 Frames Per Second

Human vision is dynamic and continuous. However, in video understanding with multimodal large language models (LLMs), existing methods primarily rel...

Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language Models

Vision-language models (VLMs) have achieved impressive progress in natural image reasoning, yet their potential in medical imaging remains underexpl...

Where do Large Vision-Language Models Look at when Answering Questions?

Large Vision-Language Models (LVLMs) have shown promising performance in vision-language understanding and reasoning tasks. However, their visual un...

RAD: Retrieval-Augmented Decision-Making of Meta-Actions with Vision-Language Models in Autonomous Driving

Accurately understanding and deciding high-level meta-actions is essential for ensuring reliable and safe autonomous driving systems. While vision-l...

MamBEV: Enabling State Space Models to Learn Birds-Eye-View Representations

3D visual perception tasks, such as 3D detection from multi-camera images, are essential components of autonomous driving and assistance systems. Ho...

Active Inference and Intentional Behavior.

Recent advances in theoretical biology suggest that key definitions of basal cognition and sentient behavior may arise as emergent properties of in vi...

Guarding Digital Privacy: Exploring User Profiling and Security Enhancements

User profiling, the practice of collecting user information for personalized recommendations, has become widespread, driving progress in technology....

Vision-Language Models for Acute Tuberculosis Diagnosis: A Multimodal Approach Combining Imaging and Clinical Data

Background: This study introduces a Vision-Language Model (VLM) leveraging SIGLIP and Gemma-3b architectures for automated acute tuberculosis (TB) s...

Advancing Chronic Tuberculosis Diagnostics Using Vision-Language Models: A Multi modal Framework for Precision Analysis

Background: This study proposes a Vision-Language Model (VLM) leveraging the SIGLIP encoder and Gemma-3b transformer decoder to enhance automated ch...

Branch Prediction Analysis of Morris-Pratt and Knuth-Morris-Pratt Algorithms

We analyze the classical Morris-Pratt and Knuth-Morris-Pratt pattern matching algorithms through the lens of computer architecture, investigating th...

Web Artifact Attacks Disrupt Vision Language Models

Vision-language models (VLMs) (e.g., CLIP, LLaVA) are trained on large-scale, lightly curated web datasets, leading them to learn unintended correla...

Lifting the Veil on Visual Information Flow in MLLMs: Unlocking Pathways to Faster Inference

Multimodal large language models (MLLMs) improve performance on vision-language tasks by integrating visual features from pre-trained vision encoder...

Aligning Vision to Language: Text-Free Multimodal Knowledge Graph Construction for Enhanced LLMs Reasoning

Multimodal reasoning in Large Language Models (LLMs) struggles with incomplete knowledge and hallucination artifacts, challenges that textual Knowle...

From Head to Tail: Towards Balanced Representation in Large Vision-Language Models through Adaptive Data Calibration

Large Vision-Language Models (LVLMs) have achieved significant progress in combining visual comprehension with language generation. Despite this suc...

TransDiff: Diffusion-Based Method for Manipulating Transparent Objects Using a Single RGB-D Image

Manipulating transparent objects presents significant challenges due to the complexities introduced by their reflection and refraction properties, w...