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

提出了一种用于医学图像中疾病定位的新方法——弱监督ROI提议网络(WSRPN)，该方法通过专门的感兴趣区域-注意力模块实时生成边界框提议。在胸部X射线图像的具有挑战性的疾病定位任务中，实验证明该方法优于现有方法。

在胸部X光中使用可区分的ROI提议网络和软ROI池化进行弱监督对象检测