计算机科学
假阳性悖论
特征(语言学)
超参数
人工智能
阶段(地层学)
灵敏度(控制系统)
节点(物理)
手术计划
假阳性和假阴性
模式识别(心理学)
计算机视觉
放射科
医学
生物
工程类
哲学
结构工程
古生物学
语言学
电子工程
作者
Yi Zhang,Jiayue Li,Xinyang Li,Mei Xie,Md Tauhidul Islam,Haixian Zhang
出处
期刊:IEEE Transactions on Medical Imaging
[Institute of Electrical and Electronics Engineers]
日期:2024-03-01
卷期号:43 (3): 1180-1190
标识
DOI:10.1109/tmi.2023.3329464
摘要
Accurate and automatic detection of pelvic lymph nodes in computed tomography (CT) scans is critical for diagnosing lymph node metastasis in colorectal cancer, which in turn plays a crucial role in its staging, treatment planning, surgical guidance, and postoperative follow-up of colorectal cancer. However, achieving high detection sensitivity and specificity poses a challenge due to the small and variable sizes of these nodes, as well as the presence of numerous similar signals within the complex pelvic CT image. To tackle these issues, we propose a 3D feature-aware online-tuning network (FAOT-Net) that introduces a novel 1.5-stage structure to seamlessly integrate detection and refinement via our online candidate tuning process and takes advantage of multi-level information through the tailored feature flow. Furthermore, we redesign the anchor fitting and anchor matching strategies to further improve detection performance in a nearly hyperparameter-free manner. Our framework achieves the FROC score of 52.8 and the sensitivity of 91.7% with 16 false positives per scan on the PLNDataset. Code will be available at: github.com/SCUsomebody/FAOT-Net/.
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