Reliable classification and detection of certain medical conditions, in images, with state-of-the-art semantic segmentation networks, require vast amounts of pixel-wise annotation. However, the public availability of such datasets is minimal. Therefore, semantic segmentation with image-level labels presents a promising alternative to this problem. Nevertheless, very few works have focused on evaluating this technique and its applicability to the medical sector. Due to their complexity and the small number of training examples in medical datasets, classifier-based weakly supervised networks like class activation maps (CAMs) struggle to extract useful information from them. However, most state-of-the-art approaches rely on them to achieve their improvements. Therefore, we propose a framework that can still utilize the low-quality CAM predictions of complicated datasets to improve the accuracy of our results. Our framework achieves that by first utilizing lower threshold CAMs to cover the target object with high certainty; second, by combining multiple low-threshold CAMs that even out their errors while highlighting the target object. We performed exhaustive experiments on the popular multi-modal BRATS and prostate DECATHLON segmentation challenge datasets. Using the proposed framework, we have demonstrated an improved dice score of up to 8% on BRATS and 6% on DECATHLON datasets compared to the previous state-of-the-art.

翻译：可靠的医学图像分类与检测需要借助最先进的语义分割网络，但这类方法依赖于大量的像素级标注数据。然而，此类数据集的公开可用性极为有限。因此，基于图像级标签的语义分割为此问题提供了有前景的替代方案。尽管如此，目前鲜有研究关注该技术及其在医学领域的适用性。由于医学数据集的复杂性及训练样本数量稀少，基于分类器的弱监督网络（如类激活映射）难以从中提取有效信息。然而，大多数最先进的方法仍依赖此类网络实现性能提升。为此，我们提出一种框架，能够利用复杂数据集生成的低质量类激活映射预测结果来提升精度。该框架首先通过采用较低阈值的类激活映射高置信度覆盖目标对象，其次融合多个低阈值类激活映射，在突出目标对象的同时平衡其误差。我们在多模态BRATS和前列腺DECATHLON分割挑战数据集上进行了全面实验。与现有最优方法相比，所提框架在BRATS和DECATHLON数据集上的Dice评分分别提升了8%和6%。