bibtext: @InProceedings{ Cha_QueryNet_MICCAI2024,
                   author = { Chai, Jiaxing and Luo, Zhiming and Gao, Jianzhe and Dai, Licun and Lai, Yingxin and Li, Shaozi },
                   title = { { QueryNet: A Unified Framework for Accurate Polyp Segmentation and Detection } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15008 },
                   month = {October},
                   pages = { pending },
                }
abstract: 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.
title: QueryNet: A Unified Framework for Accurate Polyp Segmentation and Detection
authors: Chai, Jiaxing Luo, Zhiming Gao, Jianzhe Dai, Licun Lai, Yingxin Li, Shaozi