论文标题
脱钩梯度统一检测器进行部分注释：应用于标志环电池检测
Decoupled Gradient Harmonized Detector for Partial Annotation: Application to Signet Ring Cell Detection
论文作者
论文摘要
标志性环细胞癌的早期诊断大大提高了患者的存活率。由于缺乏公共数据集和专家级注释，尚未对Signet Ring Cell（SRC）自动检测进行彻底研究。在Miccai DigestPath2019挑战中，除前景（SRC区域） - 背景（正常组织区域）类别不平衡，由于昂贵的医疗图像注释，SRC被部分注释，这引入了额外的标签噪声。为了同时解决这些问题，我们提出了解耦梯度协调机制（DGHM），并将其嵌入分类损失中，称为DGHM-C损失。具体而言，除了阳性（SRC）和负（正常组织）示例外，我们进一步将嘈杂的示例与干净的示例分别分离，并分别协调分类中相应的梯度分布。没有口哨和铃铛，我们在挑战中获得了第二名。消融研究和受控的标签缺失率实验表明，DGHM-C损失可以在部分注释的对象检测中实质性改善。
Early diagnosis of signet ring cell carcinoma dramatically improves the survival rate of patients. Due to lack of public dataset and expert-level annotations, automatic detection on signet ring cell (SRC) has not been thoroughly investigated. In MICCAI DigestPath2019 challenge, apart from foreground (SRC region)-background (normal tissue area) class imbalance, SRCs are partially annotated due to costly medical image annotation, which introduces extra label noise. To address the issues simultaneously, we propose Decoupled Gradient Harmonizing Mechanism (DGHM) and embed it into classification loss, denoted as DGHM-C loss. Specifically, besides positive (SRCs) and negative (normal tissues) examples, we further decouple noisy examples from clean examples and harmonize the corresponding gradient distributions in classification respectively. Without whistles and bells, we achieved the 2nd place in the challenge. Ablation studies and controlled label missing rate experiments demonstrate that DGHM-C loss can bring substantial improvement in partially annotated object detection.
