TriAug: Out-of-Distribution Detection for Imbalanced Breast Lesion in Ultrasound

• URL: http://arxiv.org/abs/2402.07452v2
• Date: Tue, 27 Feb 2024 02:19:54 GMT
• Title: TriAug: Out-of-Distribution Detection for Imbalanced Breast Lesion in Ultrasound
• Authors: Yinyu Ye, Shijing Chen, Dong Ni, Ruobing Huang
• Abstract summary: 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 which improves ID classification accuracy while maintaining a promising OOD detection performance.
• Score: 6.3267889365863414
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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%) and OOD detection (AUROC=78.06%).

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