  Accurate and automatic analysis of breast MRI plays an important role in
early diagnosis and successful treatment planning for breast cancer. Due to the
heterogeneity nature, accurate diagnosis of tumors remains a challenging task.
In this paper, we propose to identify breast tumor in MRI by Cosine Margin
Sigmoid Loss (CMSL) with deep learning (DL) and localize possible cancer lesion
by COrrelation Attention Map (COAM) based on the learned features. The CMSL
embeds tumor features onto a hypersphere and imposes a decision margin through
cosine constraints. In this way, the DL model could learn more separable
inter-class features and more compact intra-class features in the angular
space. Furthermore, we utilize the correlations among feature vectors to
generate attention maps that could accurately localize cancer candidates with
only image-level label. We build the largest breast cancer dataset involving
10,290 DCE-MRI scan volumes for developing and evaluating the proposed methods.
The model driven by CMSL achieved classification accuracy of 0.855 and AUC of
0.902 on the testing set, with sensitivity and specificity of 0.857 and 0.852,
respectively, outperforming other competitive methods overall. In addition, the
proposed COAM accomplished more accurate localization of the cancer center
compared with other state-of-the-art weakly supervised localization method.
