Index
DistNeural networks for hearing aid processing
Towards Autonomous Knowledge Evolution: A Self-Evolving Knowledge Graph-Based Retrieval Framework for Domain-Specific Intelligent Systems
Hierarchy-Aware Deep Learning for Tironian Notes Recognition
Development of a Local LLM Agent System for Clinical Expert Support and Automation in MRI Planning for Radiation Therapy
RAG-Enhanced Low-Cost Vision-Language Models for Diabetic Retinopathy Classification and Automated Reporting
Diabetic Retinopathy (DR) affects over 160 million people globally, projected to reach 180 million by 2030, despite 90% of related blindness being preventable through early detection [1]. Current AI models achieve strong classification performance but lack interpretable clinical reports, limiting their adoption in low-resource settings. Although Vision-Language Models (VLMs) offer unified diagnosis and report generation, fundus captioning significantly underperforms compared with other imaging modalities [2,3], and state-of-the-art VLMs remain computationally expensive. Although Retrieval-Augmented Generation (RAG) has improved medical imaging accuracy [4], no prior study has integrated DR severity grading, lesion-aware reporting, and evidence retrieval within a low-cost, clinically deployable VLM.
Investigating the Influence of Different Motion Sensors for Detecting Parkinson’s Disease
Integrating Transformer Networks with Multi-Modal Learning for Document Layout Analysis
A Cascaded Encoder–Decoder Network for CT Image Restoration
Evaluate LORA finetuning for detection guided segmentation in CT images
Evaluation of LoRa tuning of grounded segmentation using MedSam. We will investigate if we can reduce the training parameters for optimal detection and segmentation performnce using SOTA methods and training paradigms on an Abdominal RSNA dataset.