Automatic anatomical localization is critical for radiology report generation. While many studies focus on lesion detection and segmentation, anatomical localization-accurately describing lesion positions in radiology reports-has received less attention. Conventional segmentation-based methods are limited to organ-level localization and often fail in severe disease cases due to low segmentation accuracy. To address these limitations, we reformulate anatomical localization as an image-to-text retrieval task. Specifically, we propose a CLIP-based framework that aligns lesion image patches with anatomically descriptive text embeddings in a shared multimodal space. By projecting lesion features into the semantic space and retrieving the most relevant anatomical descriptions in a coarse-to-fine manner, our method achieves fine-grained lesion localization with high accuracy across the entire body. Our main contributions are as follows: (1) hierarchical anatomical retrieval, which organizes 387 locations into a two-level hierarchy, by retrieving from the first level of 124 coarse categories to narrow down the search space and reduce localization complexity; (2) augmented location descriptions, which integrate domain-specific anatomical knowledge for enhancing semantic representation and improving visual-text alignment; and (3) semi-hard negative sample mining, which improves training stability and discriminative learning by avoiding selecting the overly similar negative samples that may introduce label noise or semantic ambiguity. We validate our method on two whole-body PET/CT datasets, achieving an 84.13% localization accuracy on the internal test set and 80.42% on the external test set, with a per-lesion inference time of 34 ms. The proposed framework also demonstrated superior robustness in complex clinical cases compared to segmentation-based approaches.

Large Language Models (LLMs) have recently demonstrated exceptional performance in various real-world data management tasks through in-context learning (ICL), which involves structuring prompts with task descriptions and several demonstrations. However, most LLMs are not free and charge based on the number of input tokens. Specifically, for data management tasks, there may be massive related questions, leading to high inference cost due to redundant prompt content (i.e., overlapping demonstrations and repeated task descriptions). In this paper, we investigate the idea of batch prompting in leveraging LLMs for data management, which leads to cost-effective LLMs by grouping questions and demonstrations to perform inferences in batches. Current studies on batch prompting are preliminary and mostly based on heuristics, making it difficult to generalize to various types of tasks and adapt to different grouping strategies. To address these challenges, in this work we first formalize the batch prompting problem in general setting. Then, we study the hardness of this problem and propose efficient algorithms for adaptive grouping. Finally, we conduct comprehensive experiments on 14 datasets. Extensive experimental results demonstrate that our solution consistently outperforms the state-of-the-art baselines while consuming lower cost.