Model-Intrinsic Attention as Explanation for Radiology Image Captioning

Seeing, Describing, and Explaining Medical Image Understanding via Retrieval and Multimodal Learning

DS-MED Lab at ImageCLEFmedical Caption 2026: Clinical-Aware Routing for Hallucination Suppression and Pathology-Focused Medical Image Captioning

Leveraging Assistive Image Captioning on VizWiz through Multi-Caption Supervision

Most image captioning datasets provide several human captions for the same image, yet model training is frequently carried out in a way that effectively treats each image as having only one caption. Our study revisits this mismatch by examining the impact of training with multiple captions per image on caption generati...

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.

RoboSurg-VQA: A Multimodal Visual Question Answering Benchmark Derived from Surgical Segmentation Data

RoboSurg-VQA organises this information as a closed-set visual question answering (VQA) benchmark while retaining the source of each answer while retaining the source of each answer.

Vassilis Sioros at Sci-ImageMiner 2026 Tasks 2, 3, and 4

In this work, we address two core challenges in the ALD-E domain: auxiliary evidence construction via summarization and data table extraction, and visual question answering (VQA). Our approach first generates auxiliary summaries and structured tables to support test-time VQA prompting, using Qwen3.5-9B vision-language...