UIT_NEWRON at ImageCLEFmed MEDVQA-GI 2026: Canonical Post-Processing for Gastrointestinal Visual Question Answering

Overview of ImageCLEFmedical 2026 – Visual Question Answering with Explainable and Safety-Aware Reasoning for the Gastrointestinal Tract

Parameter-Efficient Vision–Language Fine-Tuning for Gastrointestinal Endoscopy VQA with Structured Explainability and Safety-Oriented Reasoning

UIT_Worker at ImageCLEFmedical 2026: DenseNet-121 Full-Fit Medical Concept Detection with Asymmetric Loss

Results indicate that a carefully optimized ImageNet-initialized DenseNet-121 with imbalance-aware learning provides an effective and reliable solution for extreme long-tail medical concept detection.

Evaluating Domain-Agnostic Retrieval-Augmented Generation for Gastrointestinal Medical VQA

MADS-VQA: MacroMamba and Dual-Sequence Feature Fusion for Medical Visual Question Answering

Medical Visual Question Answering (Med-VQA) aims to generate answers from medical images and natural language questions, supporting clinical decision-making and intelligent healthcare. However, existing methods often rely on homogeneous semantic modeling, limiting their ability to capture global anatomical structures a...

NUSTPB at ImageCLEFmed Caption 2026: Medical Image Captioning with QLoRA-Adapted LLaVA-1.5