Qianqian Chen

Xianjun Han, Qianqian Chen, Zhaoyang Xie et al.

The accurate segmentation of medical images is a crucial step in obtaining reliable morphological statistics. However, training a deep neural network for this task requires a large amount of labeled data to ensure high-accuracy results. To address this issue, we propose using progressive text prompts as prior knowledge to guide the segmentation process. Our model consists of two stages. In the first stage, we perform contrastive learning on natural images to pretrain a powerful prior prompt encoder (PPE). This PPE leverages text prior prompts to generate multimodality features. In the second stage, medical image and text prior prompts are sent into the PPE inherited from the first stage to achieve the downstream medical image segmentation task. A multiscale feature fusion block (MSFF) combines the features from the PPE to produce multiscale multimodality features. These two progressive features not only bridge the semantic gap but also improve prediction accuracy. Finally, an UpAttention block refines the predicted results by merging the image and text features. This design provides a simple and accurate way to leverage multiscale progressive text prior prompts for medical image segmentation. Compared with using only images, our model achieves high-quality results with low data annotation costs. Moreover, our model not only has excellent reliability and validity on medical images but also performs well on natural images. The experimental results on different image datasets demonstrate that our model is effective and robust for image segmentation.

Qianqian Chen, Anglin Liu, Jingyang Zhang et al.

Accurate brain lesion segmentation in MRI is vital for effective clinical diagnosis and treatment planning. Due to high annotation costs and strict data privacy regulations, universal models require employing Continual Learning (CL) to adapt to evolving clinical tasks without losing previously acquired knowledge. However, existing CL paradigms often suffer from capacity limits or redundant parameter growth, and even advanced dynamic methods rely mostly on image-perception strategies that struggle to handle the substantial pathological and multimodal heterogeneity inherent in brain imaging. To address this issue, we propose Concept-Reasoning Expansion (CoRE) framework, which establishes a joint decision-making mechanism by integrating visual features with structured concepts. Through the alignment of image tokens with a hierarchical concept library, CoRE simulates clinical reasoning to guide both interpretable expert routing and demand-based model growth. This collaborative process ensures model evolution is grounded in clinical priors, preventing redundant parameter expansion while maximizing knowledge reuse. Extensive evaluations across 12 sequential brain lesion MRI tasks demonstrate that CoRE achieves state-of-the-art performance and provides a high knowledge starting point for efficient future adaptation. Its superior few-shot transferability and clinical interpretability further validate its effectiveness in managing non-stationary clinical data streams. Our code will be released soon.

Anglin Liu, Rundong Xue, Xu R. Cao et al.

Medical image segmentation is fundamental for biomedical discovery. Existing methods lack generalizability and demand extensive, time-consuming manual annotation for new clinical application. Here, we propose MedSAM-3, a text promptable medical segmentation model for medical image and video segmentation. By fine-tuning the Segment Anything Model (SAM) 3 architecture on medical images paired with semantic conceptual labels, our MedSAM-3 enables medical Promptable Concept Segmentation (PCS), allowing precise targeting of anatomical structures via open-vocabulary text descriptions rather than solely geometric prompts. We further introduce the MedSAM-3 Agent, a framework that integrates Multimodal Large Language Models (MLLMs) to perform complex reasoning and iterative refinement in an agent-in-the-loop workflow. Comprehensive experiments across diverse medical imaging modalities, including X-ray, MRI, Ultrasound, CT, and video, demonstrate that our approach significantly outperforms existing specialist and foundation models. We will release our code and model at https://github.com/Joey-S-Liu/MedSAM3.

Huixiang Luo, Longyu Gao, Yaqi Liu et al.

Bid optimization in online advertising relies on black-box machine-learning models that learn bidding decisions from historical data. However, these approaches fail to replicate human experts' adaptive, experience-driven, and globally coherent decisions. Specifically, they generalize poorly in data-sparse cases because of missing structured knowledge, make short-sighted sequential decisions that ignore long-term interdependencies, and struggle to adapt in out-of-distribution scenarios where human experts succeed. To address this, we propose KBD (Knowledge-informed Bidding with Dual-process control), a novel method for bid optimization. KBD embeds human expertise as inductive biases through the informed machine-learning paradigm, uses Decision Transformer (DT) to globally optimize multi-step bidding sequences, and implements dual-process control by combining a fast rule-based PID (System 1) with DT (System 2). Extensive experiments highlight KBD's advantage over existing methods and underscore the benefit of grounding bid optimization in human expertise and dual-process control.

Qilong Han, Qianqian Chen, Kejia Zhang et al.

Collection of user's location and trajectory information that contains rich personal privacy in mobile social networks has become easier for attackers. Network traffic control is an important network system which can solve some security and privacy problems. In this paper, we consider a network traffic control system as a trusted third party and use differential privacy for protecting more personal trajectory data. We studied the influence of the high dimensionality and sparsity of trajectory data sets based on the availability of the published results. Based on similarity point aggregation reconstruction ideas and a prefix tree model, we proposed a hybrid publishing method of differential privacy spatiotemporal trajectory data sets APTB.