CT-Bench: A Benchmark for Multimodal Lesion Understanding in Computed Tomography

• URL: http://arxiv.org/abs/2602.14879v2
• Date: Thu, 19 Feb 2026 18:19:25 GMT
• Title: CT-Bench: A Benchmark for Multimodal Lesion Understanding in Computed Tomography
• Authors: Qingqing Zhu, Qiao Jin, Tejas S. Mathai, Yin Fang, Zhizheng Wang, Yifan Yang, Maame Sarfo-Gyamfi, Benjamin Hou, Ran Gu, Praveen T. S. Balamuralikrishna, Kenneth C. Wang, Ronald M. Summers, Zhiyong Lu,
• Abstract summary: CT-Bench is a first-of-its-kind benchmark dataset for lesion analysis on computed tomography (CT)<n>It consists of a Lesion Image and Metadata Set containing 20,335 lesions from 7,795 CT studies with bounding boxes, descriptions, and size information, and a visual question answering benchmark with 2,850 QA pairs covering lesion localization, description, size estimation, and attribute categorization.<n>We evaluate state-of-the-art multimodal models, including vision-language and medical CLIP variants, by comparing their performance to radiologist assessments.
• Score: 16.912129516334783
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial intelligence (AI) can automatically delineate lesions on computed tomography (CT) and generate radiology report content, yet progress is limited by the scarcity of publicly available CT datasets with lesion-level annotations. To bridge this gap, we introduce CT-Bench, a first-of-its-kind benchmark dataset comprising two components: a Lesion Image and Metadata Set containing 20,335 lesions from 7,795 CT studies with bounding boxes, descriptions, and size information, and a multitask visual question answering benchmark with 2,850 QA pairs covering lesion localization, description, size estimation, and attribute categorization. Hard negative examples are included to reflect real-world diagnostic challenges. We evaluate multiple state-of-the-art multimodal models, including vision-language and medical CLIP variants, by comparing their performance to radiologist assessments, demonstrating the value of CT-Bench as a comprehensive benchmark for lesion analysis. Moreover, fine-tuning models on the Lesion Image and Metadata Set yields significant performance gains across both components, underscoring the clinical utility of CT-Bench.

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