UNet segmentation network of COVID-19 CT images with multi-scale attention

Weakly supervised salient object detection via image category annotation

The rapid development of deep learning has made a great progress in salient object detection task. Fully supervised methods need a large number of pixel-level annotations. To avoid laborious and consuming annotation, weakly supervised methods consider low-cost annotations such as category, bounding-box, scribble, etc. Due to simple annotation and existing large-scale classification datasets, the category annotation based methods have received more attention while still suffering from inaccurate detection. In this work, we proposed one weakly supervised method with category annotation. First, we proposed one coarse object location network (COLN) to roughly locate the object of an image with category annotation. Second, we refined the coarse object location to generate pixel-level pseudo-labels and proposed one quality check strategy to select high quality pseudo labels. To this end, we studied COLN twice followed by refinement to obtain a pseudo-labels pair and calculated the consistency of pseudo-label pairs to select high quality labels. Third, we proposed one multi-decoder neural network (MDN) for saliency detection supervised by pseudo-label pairs. The loss of each decoder and between decoders are both considered. Last but not least, we proposed one pseudo-labels update strategy to iteratively optimize pseudo-labels and saliency detection models. Performance evaluation on four public datasets shows that our method outperforms other image category annotation based work.