  Breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays
an important role in the screening and prognosis assessment of high-risk breast
cancer. The segmentation of cancerous regions is essential useful for the
subsequent analysis of breast MRI. To alleviate the annotation effort to train
the segmentation networks, we propose a weakly-supervised strategy using
extreme points as annotations for breast cancer segmentation. Without using any
bells and whistles, our strategy focuses on fully exploiting the learning
capability of the routine training procedure, i.e., the train - fine-tune -
retrain process. The network first utilizes the pseudo-masks generated using
the extreme points to train itself, by minimizing a contrastive loss, which
encourages the network to learn more representative features for cancerous
voxels. Then the trained network fine-tunes itself by using a similarity-aware
propagation learning (SimPLe) strategy, which leverages feature similarity
between unlabeled and positive voxels to propagate labels. Finally the network
retrains itself by employing the pseudo-masks generated using previous
fine-tuned network. The proposed method is evaluated on our collected DCE-MRI
dataset containing 206 patients with biopsy-proven breast cancers. Experimental
results demonstrate our method effectively fine-tunes the network by using the
SimPLe strategy, and achieves a mean Dice value of 81%.
