解决CT影像中病灶检测的类别不平衡问题，提升小类病灶检出率。
Class Imbalance Correction for Improved Universal Lesion Detection and Tagging in CT
- 按解剖部位、患者数量和病灶大小三类方式平衡数据集
- 对小样本类别（如骨、肾）的1cm以上病灶敏感度提升至80%以上
- 提出放射科报告中的病灶描述标准化模板，适合临床应用
放射科医生常在CT中检测和测量病灶以评估癌症分期与肿瘤负荷。为辅助该工作，已有多个病灶检测算法基于公开数据集DeepLesion（含32,735个病灶、32,120张切片、10,594例研究、4,427名患者、8类解剖部位标签）开发。但该数据集存在缺失测量值与标签问题，且各类别病灶数量严重失衡。本文使用DeepLesion的子集（6%，共1,331个病灶、1,309张切片）训练VFNet模型实现病灶检测与解剖部位标签。通过三种策略缓解类别不平衡：按解剖部位、按患者病灶数、按病灶大小平衡数据。相比随机采样（未平衡）子集，按解剖部位平衡后，低样本类别（如骨、肾、软组织、盆腔）中≥1cm病灶的敏感度显著提升（骨：80% vs. 46%；肾：77% vs. 61%；软组织：70% vs. 60%；盆腔：83% vs. 76%）。其他三模型（FasterRCNN、RetinaNet、FoveaBox）也呈现类似趋势。按病灶大小平衡同样提升了所有类别的召回率。此外，本文还提出一份结构化报告指南，用于放射科报告“发现”部分的“病灶”子项。
原文摘要 · Abstract (English)
Radiologists routinely detect and size lesions in CT to stage cancer and assess tumor burden. To potentially aid their efforts, multiple lesion detection algorithms have been developed with a large public dataset called DeepLesion (32,735 lesions, 32,120 CT slices, 10,594 studies, 4,427 patients, 8 body part labels). However, this dataset contains missing measurements and lesion tags, and exhibits a severe imbalance in the number of lesions per label category. In this work, we utilize a limited subset of DeepLesion (6\%, 1331 lesions, 1309 slices) containing lesion annotations and body part label tags to train a VFNet model to detect lesions and tag them. We address the class imbalance by conducting three experiments: 1) Balancing data by the body part labels, 2) Balancing data by the number of lesions per patient, and 3) Balancing data by the lesion size. In contrast to a randomly sampled (unbalanced) data subset, our results indicated that balancing the body part labels always increased sensitivity for lesions >= 1cm for classes with low data quantities (Bone: 80\% vs. 46\%, Kidney: 77\% vs. 61\%, Soft Tissue: 70\% vs. 60\%, Pelvis: 83\% vs. 76\%). Similar trends were seen for three other models tested (FasterRCNN, RetinaNet, FoveaBox). Balancing data by lesion size also helped the VFNet model improve recalls for all classes in contrast to an unbalanced dataset. We also provide a structured reporting guideline for a ``Lesions'' subsection to be entered into the ``Findings'' section of a radiology report. To our knowledge, we are the first to report the class imbalance in DeepLesion, and have taken data-driven steps to address it in the context of joint lesion detection and tagging.
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