2026/08/03 by Ruida Cheng, Tejas S. Mathai, Benjamin Hou +4
Computer Science · #cs.CV #cs.AI
18 pages, 8 figures
arxiv created 2026/08/03 · arxiv updated 2026/08/05
In previous work, we integrated large language models (LLMs) into the lesion segmentation model based on the ULS23 DeepLesion dataset, using short-form findings from the reports. In this study, we developed a unified 2D lesion analysis framework that integrates LLM-based reasoning, lesion bounding box detection, segmentation, and radiology report generation from the original DeepLesion dataset. In the testing phase, we achieved relatively high lesion bounding box detection accuracy with mAP50 of 70.1%, mAP50-95 of 46.4%; Lesion segmentation performance with a Dice score of 62.6%; short report generation accuracy with BLEU1 score of 64.3%, BLEU4 score of 49.6%, METEOR of 34.7%, and ROUGEL of 60.1%. In this work, we address the challenging issue of segmentation in the original DeepLesion dataset and achieve a 28.5% Dice score improvement over the nnUNet lesion segmentation model. We also integrated spatial and anatomical context into the DeepLesion short report generation. We released the implementation, dataset, and models on Github. https://github.com/ruida/2DDeepLesionFoundation