High resolution weakly supervised localization architectures for medical images

Abstract

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.

submitted to ICASSP 2021

References in corpus (7)

• Rethinking Atrous Convolution for Semantic Image Segmentation
• Learning Important Features Through Propagating Activation Differences
• CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
• PadChest: A large chest x-ray image dataset with multi-label annotated reports
• Making Convolutional Networks Shift-Invariant Again
• Pyramid Attention Network for Semantic Segmentation
• Weakly Supervised Medical Diagnosis and Localization from Multiple Resolutions