Before revision:  To capture the similarities between the unlabeled image regions and labeled nuclei, we apply decomposed self-attention to the learned image features. By carefully examining the self-attention map of the labeled nuclei , we observe strong activations on the centers and edges of all nuclei in the image, including those unlabeled . Based on this ,  our region proposal module propagates the partial annotations to the whole image , and then generates effective bounding box proposals for the bounding box regression and binary mask generation modules.
After revision:  To capture the similarities between the unlabeled image regions and labeled nuclei, we apply decomposed self-attention to the learned features. On the self-attention map of the labeled nuclei , we observe strong activations on the centers and edges of all nuclei in the image, including those unlabeled . Based on this , our region proposal module propagates the partial annotations to the whole image , and then generates effective bounding box proposals for the bounding box regression and binary mask generation modules.