Shangqing Liu

Jun Ma, Yao Zhang, Song Gu et al.

With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved comparable results with inter-rater variability on many benchmark datasets. However, most of the existing abdominal datasets only contain single-center, single-phase, single-vendor, or single-disease cases, and it is unclear whether the excellent performance can generalize on diverse datasets. This paper presents a large and diverse abdominal CT organ segmentation dataset, termed AbdomenCT-1K, with more than 1000 (1K) CT scans from 12 medical centers, including multi-phase, multi-vendor, and multi-disease cases. Furthermore, we conduct a large-scale study for liver, kidney, spleen, and pancreas segmentation and reveal the unsolved segmentation problems of the SOTA methods, such as the limited generalization ability on distinct medical centers, phases, and unseen diseases. To advance the unsolved problems, we further build four organ segmentation benchmarks for fully supervised, semi-supervised, weakly supervised, and continual learning, which are currently challenging and active research topics. Accordingly, we develop a simple and effective method for each benchmark, which can be used as out-of-the-box methods and strong baselines. We believe the AbdomenCT-1K dataset will promote future in-depth research towards clinical applicable abdominal organ segmentation methods. The datasets, codes, and trained models are publicly available at https://github.com/JunMa11/AbdomenCT-1K.

Yanzhou Li, Tianlin Li, Yiran Zhang et al.

Large Language Models (LLMs) are increasingly used for function completion in repository-scale codebases. Prior studies demonstrate that when explicit instructions--such as docstrings--are provided, these models can generate highly accurate implementations. However, in real-world repositories, such annotations are frequently absent, and performance drops substantially without them. To address this gap, we frame the task as a three-stage process. The first stage focuses on intent inference, where the model analyzes the code preceding the target function to uncover cues about the desired functionality. Such preceding context often encodes subtle but critical information, and we design a reasoning-based prompting framework to guide the LLM through step-by-step extraction and synthesis of these signals before any code is generated. The second stage introduces an optional interactive refinement mechanism to handle cases where preceding context alone is insufficient for intent recovery. In this stage, the model proposes a small set of candidate intentions, enabling the developer to select or edit them so that the inferred intent closely matches the actual requirement. Finally, in the third stage, the LLM generates the target function conditioned on the finalized intent. To support this pipeline, we curate a dataset of 40,000 examples annotated with intermediate reasoning traces and corresponding docstrings. Extensive experiments on DevEval and ComplexCodeEval show that our approach consistently boosts multiple LLMs, achieving over 20\% relative gains in both reference-based and execution-based metrics, with the interactive refinement stage delivering additional improvements beyond these gains.