Weakly supervised object detection (WSup-OD) increases the usefulness and interpretability of image classification algorithms without requiring additional supervision. The successes of multiple instance learning in this task for natural images, however, do not translate well to medical images due to the very different characteristics of their objects (i.e. pathologies). In this work, we propose Weakly Supervised ROI Proposal Networks (WSRPN), a new method for generating bounding box proposals on the fly using a specialized region of interest-attention (ROI-attention) module. WSRPN integrates well with classic backbone-head classification algorithms and is end-to-end trainable with only image-label supervision. We experimentally demonstrate that our new method outperforms existing methods in the challenging task of disease localization in chest X-ray images. Code: https://github.com/philip-mueller/wsrpn

翻译：弱监督目标检测（WSup-OD）可在无需额外监督信号的前提下提升图像分类算法的实用性与可解释性。然而，多实例学习在自然图像任务中的成功经验难以直接迁移至医学图像领域，原因在于两者的目标对象（即病理特征）具有迥异特性。本文提出弱监督ROI提议网络（WSRPN），这是一种通过专用感兴趣区域注意力（ROI-attention）模块动态生成边界框提议的新方法。WSRPN能够与经典的主干-分类头算法良好集成，且仅需图像级标签监督即可实现端到端训练。实验证明，本方法在胸部X光片疾病定位这一具有挑战性的任务中优于现有方法。代码：https://github.com/philip-mueller/wsrpn