Abstract
Weakly supervised object localization (WSOL) methods allow training models to classify images and localize ROIs. WSOL only requires low-cost image-class annotations yet provides a visually interpretable classifier, which is important in histology image analysis. Standard WSOL methods rely on class activation mapping (CAM) methods to produce spatial localization maps according to a single- or two-step strategy. While both strategies have made significant progress, they still face several limitations with histology images. Single-step methods can easily result in under- or over-activation due to the limited visual ROI saliency in histology images and the limited localization cues. They also face the well-known issue of asynchronous convergence between classification and localization tasks. The two-step approach is sub-optimal because it is tied to a frozen classifier, limiting the capacity for localization. Moreover, these methods also struggle when applied to out-of-distribution (OOD) datasets. In this paper, a multi-task approach for WSOL is introduced for simultaneous training of both tasks to address the asynchronous convergence problem. In particular, localization is performed in the pixel-feature space of an image encoder that is shared with classification. This allows learning discriminant features and accurate delineation of foreground/background regions to support ROI localization and image classification. We propose PixelCAM, a cost-effective foreground/background pixel-wise classifier in the pixel-feature space that allows for spatial object localization. PixelCAM is trained using pixel pseudo-labels collected from a pretrained WSOL model. Both image and pixel-wise classifiers are trained simultaneously using standard gradient descent. In addition, our pixel classifier can easily be integrated into CNN- and transformer-based architectures without any modifications.
Abstract (translated)
弱监督目标定位（WSOL）方法允许在对图像进行分类的同时，训练模型来定位感兴趣区域（ROI）。与标准的完全标注数据集相比，WSOL仅需要低成本的图像类别标签，这为医学组织学图像分析提供了重要的视觉可解释性。传统的WSOL方法依赖于类激活图（CAM）技术，通过单步或两步策略生成空间定位地图。 虽然这两种策略都取得了显著进展，但在处理组织学图像时仍面临诸多局限。单步法由于组织学图像中ROI的可视性有限和局部化线索不足，容易导致过强或过弱的激活现象，并且也存在分类任务与定位任务之间异步收敛的问题。两步方法则因为依赖于固定的分类器而难以优化，限制了其在定位方面的表现能力。此外，这些传统方法当应用于分布外（OOD）数据集时也会遇到困难。 本文介绍了一种用于WSOL的多任务方法，通过同时训练两个任务以解决异步收敛问题。具体来说，在一个与分类共享权重的图像编码器像素特征空间中执行定位操作，从而允许学习区分性特征并精确地划分前景/背景区域，支持ROI定位和图像分类。 我们提出了一种名为PixelCAM的方法，它是在像素特征空间中的成本效益型前景/背景像素级分类器，可以用于实现空间物体的定位。PixelCAM通过使用从预训练WSOL模型收集到的像素伪标签进行训练，并且图像和像素级分类器同时采用标准梯度下降法训练。此外，我们的像素分类器能够轻松集成到基于CNN或transformer架构中，无需任何修改。 这种方法不仅在组织学图像上表现良好，在处理分布外数据集时也显示出更优的鲁棒性和适用性。
URL
https://arxiv.org/abs/2503.24135
