Weakly Supervised Organ Localization with Attention Maps Regularized by Local Area Reconstruction

Fully supervised methods with numerous dense-labeled training data have achieved accurate localization results for anatomical structures. However, obtaining such a dedicated dataset usually requires clinical expertise and time-consuming annotation process. In this work, we tackle the organ localization problem under the setting of image-level annotations. Previous Class Activation Map (CAM) and its derivatives have proved that discriminative regions of images can be located with basic classification networks. To improve the representative capacity of attention maps generated by CAMs, a novel learning-based Local Area Reconstruction (LAR) method is proposed. Our weakly supervised organ localization network, namely OLNet, can generate high-resolution attention maps that preserve fine-detailed target anatomical structures. Online generated pseudo ground-truth is utilized to impose geometric constraints on attention maps. Extensive experiments on In-house Chest CT Dataset and Kidney Tumor Segmentation Benchmark (KiTS19) show that our approach can provide promising localization results both in saliency map and semantic segmentation perspectives.