Weakly Supervised Lesion Localization With Probabilistic-CAM Pooling
This addresses the challenge of reducing annotation costs for medical imaging tasks, though it is an incremental improvement over existing weakly supervised methods.
The paper tackles the problem of localizing thoracic diseases in chest X-rays without requiring bounding box annotations by introducing Probabilistic-CAM pooling, which improves both classification and localization performance on the ChestX-ray14 dataset compared to state-of-the-art baselines.
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.