PDSE：基于PANet与可变形压缩激励模块的CT图像多病灶检测器
PDSE: A Multiple Lesion Detector for CT Images using PANet and Deformable Squeeze-and-Excitation Block
AI总结：
本文提出一阶段CT病灶检测框架PDSE，通过重新设计Retinanet、融入低层特征图并利用自适应SE模块与通道注意力，在DeepLesion基准上取得超过0.20的mAP，显著提升小目标和多尺度病灶检测性能。
摘要：
在计算机断层扫描（CT）中检测病灶是医学图像处理中的一项具有挑战性的任务，原因在于病灶的类型、大小和位置多种多样。近年来，人们开发了各种一阶段和两阶段框架网络，专注于病灶定位。我们引入了一种一阶段病灶检测框架PDSE，通过重新设计Retinanet，在多模态CT图像中实现更高的病灶检测精度和效率。具体来说，我们通过融入低层特征图来增强路径聚合流。此外，为了提升模型表示能力，我们利用自适应Squeeze-and-Excitation（SE）模块并整合通道特征图注意力。这一方法取得了新的最先进性能。我们的方法显著改善了对小目标和多尺度目标的检测。在公开的DeepLesion基准上与其他先进算法进行评估时，我们的算法取得了超过0.20的mAP。
英文摘要：
Detecting lesions in Computed Tomography (CT) scans is a challenging task in medical image processing due to the diverse types, sizes, and locations of lesions. Recently, various one-stage and two-stage framework networks have been developed to focus on lesion localization. We introduce a one-stage lesion detection framework, PDSE, by redesigning Retinanet to achieve higher accuracy and efficiency for detecting lesions in multimodal CT images. Specifically, we enhance the path aggregation flow by incorporating a low-level feature map. Additionally, to improve model representation, we utilize the adaptive Squeeze-and-Excitation (SE) block and integrate channel feature map attention. This approach has resulted in achieving new state-of-the-art performance. Our method significantly improves the detection of small and multiscaled objects. When evaluated against other advanced algorithms on the public DeepLesion benchmark, our algorithm achieved an mAP of over 0.20.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
