  Different diseases, such as histological subtypes of breast lesions, have
severely varying incidence rates. Even trained with substantial amount of
in-distribution (ID) data, models often encounter out-of-distribution (OOD)
samples belonging to unseen classes in clinical reality. To address this, we
propose a novel framework built upon a long-tailed OOD detection task for
breast ultrasound images. It is equipped with a triplet state augmentation
(TriAug) which improves ID classification accuracy while maintaining a
promising OOD detection performance. Meanwhile, we designed a balanced sphere
loss to handle the class imbalanced problem. Experimental results show that the
model outperforms state-of-art OOD approaches both in ID classification
(F1-score=42.12%) and OOD detection (AUROC=78.06%).
