论文标题
通过概率摄像头池的弱监督病变定位
Weakly Supervised Lesion Localization With Probabilistic-CAM Pooling
论文作者
论文摘要
在胸部X射线上定位胸部疾病在诊断和治疗计划等临床实践中起着至关重要的作用。但是，当前基于深度学习的方法通常需要强大的监督，例如带注释的边界盒，用于训练此类系统，大规模收获是不可行的。我们提出了概率类激活图（PCAM）池，这是一种新型的全局池化操作，用于病变定位，仅具有图像级监督。 PCAM合并明确利用了CAM在以概率方式进行培训期间的出色定位能力。 ChestX-Ray14数据集上的实验显示了一个Resnet-34型号，该模型在分类任务和本地化任务上都在PCAM池池中优于最先进的基线。与CAM产生的定位热图相比，PCAM合并产生的概率图的视觉检查显示了病变区域周围清晰而锐利的边界。 PCAM池在https://github.com/jfhealthcare/chexpert上开放。
Localizing thoracic diseases on chest X-ray plays a critical role in clinical practices such as diagnosis and treatment planning. However, current deep learning based approaches often require strong supervision, e.g. annotated bounding boxes, for training such systems, which is infeasible to harvest in large-scale. We present Probabilistic Class Activation Map (PCAM) pooling, a novel global pooling operation for lesion localization with only image-level supervision. PCAM pooling explicitly leverages the excellent localization ability of CAM during training in a probabilistic fashion. Experiments on the ChestX-ray14 dataset show a ResNet-34 model trained with PCAM pooling outperforms state-of-the-art baselines on both the classification task and the localization task. Visual examination on the probability maps generated by PCAM pooling shows clear and sharp boundaries around lesion regions compared to the localization heatmaps generated by CAM. PCAM pooling is open sourced at https://github.com/jfhealthcare/Chexpert.
