High resolution weakly supervised localization architectures for medical images

High resolution weakly supervised localization architectures for medical images

In medical imaging, Class-Activation Map (CAM) serves as the main explainability tool by pointing to the region of interest. Since the localization accuracy from CAM is constrained by the resolution of the model's feature map, one may expect that segmentation models, which generally have large feature maps, would produce more accurate CAMs.However, we have found that this is not the case due to task mismatch. While segmentation models are developed for datasets with pixel-level annotation, only image-level annotation is available in most medical imaging datasets. Our experiments suggest that Global Average Pooling (GAP) and Group Normalization are the main culprits that worsen the localization accuracy of CAM. To address this issue, we propose Pyramid Localization Network (PYLON), a model for high-accuracy weakly-supervised localization that achieved 0.62 average point localization accuracy on NIH's Chest X-Ray 14 dataset, compared to 0.45 for a traditional CAM model. Source code and extended results are available at https://github.com/cmb-chula/pylon.

高分辨率弱监督医学图像的本地化体系结构

在医学成像中，类激活图（CAM）通过指向感兴趣的区域而成为主要的可解释性工具。由于CAM的定位精度受到模型特征图分辨率的限制，因此人们可能会期望通常具有较大特征图的分割模型将产生更精确的CAM。.. 但是，我们发现由于任务不匹配，情况并非如此。虽然针对具有像素级注释的数据集开发了分割模型，但大多数医学成像数据集中只有图像级注释可用。我们的实验表明，全局平均池（GAP）和组归一化是使CAM定位精度变差的主要原因。为了解决这个问题，我们提出了金字塔本地化网络（PYLON），这是一种用于高精度弱监督定位的模型，该模型在NIH的Chest X-Ray 14数据集上实现了0.62的平均点定位精度，而传统的CAM模型为0.45。源代码和扩展结果可从https://github.com/cmb-chula/pylon获得。 （阅读更多）