TriAug: Out-of-Distribution Detection for Imbalanced Breast Lesion in Ultrasound
<|reference_start|>TriAug: Out-of-Distribution Detection for Imbalanced Breast Lesion in Ultrasound: 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%).<|reference_end|>
@article{ye2024triaug:,
    title={TriAug: Out-of-Distribution Detection for Imbalanced Breast Lesion in
  Ultrasound},
    author={Yinyu Ye, Shijing Chen, Dong Ni, Ruobing Huang},
    journal={arXiv preprint arXiv:2402.07452},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.07452},
    primaryClass={cs.CV cs.AI cs.LG}
}