Haixia Pan

Gaoyu Chen, Haixia Pan

Medical image segmentation plays a crucial role in clinical medicine, serving as a tool for auxiliary diagnosis, treatment planning, and disease monitoring, thus facilitating physicians in the study and treatment of diseases. However, existing medical image segmentation methods are limited by the weak semantic expression of the target segmentation regions, which is caused by the low contrast between the target and non-target segmentation regions. To address this limitation, text prompt information has greast potential to capture the lesion location. However, existing text-guided methods suffer from insufficient cross-modal interaction and inadequate cross-modal feature expression. To resolve these issues, we propose the Text-guided Multi-stage Cross-perception network (TMC). In TMC, we introduce a multistage cross-attention module to enhance the model's understanding of semantic details and a multi-stage alignment loss to improve the consistency of cross-modal semantics. The results of the experiments demonstrate that our TMC achieves a superior performance with Dice of 84.77%, 78.50%, 88.73% in three public datasets (QaTa-COV19, MosMedData and Breast), outperforming UNet based networks and text-guided methods.

Jiahua Lan, Sen Zhang, Haixia Pan et al.

In contrast to the human ability to continuously acquire knowledge, agents struggle with the stability-plasticity dilemma in deep reinforcement learning (DRL), which refers to the trade-off between retaining existing skills (stability) and learning new knowledge (plasticity). Current methods focus on balancing these two aspects at the network level, lacking sufficient differentiation and fine-grained control of individual neurons. To overcome this limitation, we propose Neuron-level Balance between Stability and Plasticity (NBSP) method, by taking inspiration from the observation that specific neurons are strongly relevant to task-relevant skills. Specifically, NBSP first (1) defines and identifies RL skill neurons that are crucial for knowledge retention through a goal-oriented method, and then (2) introduces a framework by employing gradient masking and experience replay techniques targeting these neurons to preserve the encoded existing skills while enabling adaptation to new tasks. Numerous experimental results on the Meta-World and Atari benchmarks demonstrate that NBSP significantly outperforms existing approaches in balancing stability and plasticity.