arXiv subjects

Jimin Sung

Publications and source records attributed to Jimin Sung.

2 recordsLinked to original sources

arXiv2025

Region-Aware Multimodal Large Language Model via SlowFast Tokenization and Pseudo-Mask Guidance for 3D CT Report Generation

Current CT report generation frameworks predominantly rely on global feature representations, often failing to capture region-specific details and potentially missing certain abnormalities. To overcome this limitation, we propose MedRegion-CT, a region-focused multimodal large language model framework featuring three key innovations. First, we revisit the SlowFast strategy to jointly model global and fine-grained information and adapt it to the medical domain via a Region-based SlowFast Tokenizer that extracts tokens guided by clinically meaningful regions. Second, generated pseudo-masks guide the model to attend to diagnostically important anatomical regions, facilitating a systematic understanding of the overall scan context. Third, quantitative lesion information, including size, diameter, and spatial location, is encoded as structured textual prompts, enabling context-aware and clinically informed report generation. To enable rigorous evaluation, we validate our framework on multi-institutional structured report generation benchmarks. Experimental results demonstrate that MedRegion-CT achieves state-of-the-art performance, outperforming existing approaches in both linguistic quality and clinical accuracy. All code is publicly available at: https://github.com/babbu3682/MedRegion-CT.

eess.IV↗

arXiv2025

MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports

Computed Tomography (CT) plays a crucial role in clinical diagnosis, but the growing demand for CT examinations has raised concerns about diagnostic errors. While Multimodal Large Language Models (MLLMs) demonstrate promising comprehension of medical knowledge, their tendency to produce inaccurate information highlights the need for rigorous validation. However, existing medical visual question answering (VQA) benchmarks primarily focus on simple visual recognition tasks, lacking clinical relevance and failing to assess expert-level knowledge. We introduce MedErr-CT, a novel benchmark for evaluating medical MLLMs' ability to identify and correct errors in CT reports through a VQA framework. The benchmark includes six error categories - four vision-centric errors (Omission, Insertion, Direction, Size) and two lexical error types (Unit, Typo) - and is organized into three task levels: classification, detection, and correction. Using this benchmark, we quantitatively assess the performance of state-of-the-art 3D medical MLLMs, revealing substantial variation in their capabilities across different error types. Our benchmark contributes to the development of more reliable and clinically applicable MLLMs, ultimately helping reduce diagnostic errors and improve accuracy in clinical practice. The code and datasets are available at https://github.com/babbu3682/MedErr-CT.

cs.CV↗