实验室2篇论文被MICCAI-24接收。MICCAI会议是医学图像处理领域最重要的国际会议之一，每年举办一届。MICCAI-24将于2024年10月6-10日在马拉喀什 / 摩洛哥举行。

题目：QueryNet: A Unified Framework for Accurate Polyp Segmentation and Detection

作者：Jiaxing Chai, Zhiming Luo, Jianzhe Gao, Licun Dai, Yingxin Lai, Shaozi Li

摘要：Recently, deep learning-based methods have demonstrated effectiveness in the diagnosing of polyps, which holds clinical significance in the prevention of colorectal cancer. These methods can be broadly categorized into two tasks: Polyp Segmentation (PS) and Polyp Detection (PD). The advantage of PS lies in precise localization, but it is constrained by the contrast of the polyp area. On the other hand, PD provides the advantages of global perspective but is susceptible to issues such as false positives or missed detections. Despite substantial progress in both tasks, there has been limited exploration of integrating these two tasks. To address this problem, we introduce QueryNet, a unified framework for accurate polyp segmentation and detection. Specially, our QueryNet is constructed on top of Mask2Former, a query-based segmentation model. It conceptualizes object queries as cluster centers and constructs a detection branch to handle both tasks. Extensive quantitative and qualitative experiments on five public benchmarks verify that this unified framework effectively mitigates the task-specific limitations, thereby enhancing the overall performance. Furthermore, QueryNet achieves comparable performance against state-of-the-art PS and PD methods.

题目：VCLIPSeg: Voxel-wise CLIP-Enhanced model for Semi-Supervised Medical Image Segmentation

作者：Lei Li, Sheng Lian, Zhiming Luo, Beizhan Wang, Shaozi Li

摘要：Semi-supervised learning has emerged as a critical approach for addressing medical image segmentation with limited annotation, and pseudo labeling-based methods made significant progress for this task. However, the varying quality of pseudo labels poses a challenge to model generalization. In this paper, we propose a Voxel-wise CLIP-enhanced model for semi-supervised medical image Segmentation (VCLIPSeg). Our model incorporates three modules: Voxel-Wise Prompts Module (VWPM), Vision-Text Consistency Module (VTCM), and Dynamic Labeling Branch (DLB). The VWPM integrates CLIP embeddings in a voxel-wise manner, learning the semantic relationships among pixels. The VTCM constrains the image prototype features, reducing the impact of noisy data. The DLB adaptively generates pseudo-labels, effectively leveraging the unlabeled data. Experimental results on the Left Atrial (LA) dataset and Pancreas-CT dataset demonstrate the superiority of our method over state-of-the-art approaches in terms of the Dice score. For instance, it achieves a Dice score of 88.51% using only 5% labeled data from the LA dataset.