Automated lesion segmentation through breast ultrasound (BUS) images is an essential prerequisite in computer-aided diagnosis. However, the task of breast segmentation remains challenging, due to the time-consuming and labor-intensive process of acquiring precise labeled data, as well as severely ambiguous lesion boundaries and low contrast in BUS images. In this article, we propose a novel semi-supervised breast segmentation framework based on confidence-ranked features and bi-level prototypes (CoBiNet) to alleviate these issues. Our outputs are derived from two branches: classifier and projector. In the projector branch, we first rank the features by multilevel sampling to obtain multiple feature sets with different confidence levels. Then, these sets are progressed in two directions. One is to acquire local prototypes at each level by local sampling and perform trans-confidence level (TCL) contrastive learning. This encourages the low-confidence features to converge to the high-confidence features, which enhances the model's ability to recognize ambiguous regions. The other process is to generate more representative global prototypes by global sampling, followed by generating more reliable predictions and performing cross-guidance (CG) consistency learning with the classifier output predictions, facilitating knowledge transfer between the structure-aware projector and the category-discriminative classifier branches. Extensive experiments on two well-known public datasets, BUSI and UDIAT, demonstrate the superiority of our method over state-of-the-art approaches. Codes will be released upon publication.

We present a novel semi-supervised framework for breast ultrasound (BUS) image segmentation, which is a very challenging task owing to (1) large scale and shape variations of breast lesions and (2) extremely ambiguous boundaries caused by massive speckle noise and artifacts in BUS images. While existing models achieved certain progress in this task, we believe the main bottleneck nowadays for further improvement is that we still cannot deal with hard cases well. Our framework aims to break through this bottleneck, which includes two innovative components: an adaptive patch augmentation scheme and a hard-patch contrastive learning module. We first identify hard patches by computing the average entropy of each patch and then shield hard patches to prevent them from being cropped out while performing random patch cutmix. Such a scheme is able to prevent hard regions from being inadequately trained under strong augmentation. We further develop a new hard-patch contrastive learning algorithm to direct model attention to hard regions by applying extra contrast to pixels in hard patches, further improving segmentation performance on hard cases. We demonstrate the superior-ity of our framework to state-of-the-art approaches on two famous BUS datasets, achieving better performance under different labeling conditions. The code is available at https://github.com/jjjsyyy/PH-Net.