TriAug: Out-of-Distribution Detection for Imbalanced Breast Lesion in Ultrasound
arxiv(2024)
Abstract
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
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