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Multi-contrast learning-guided lightweight few-shot learning scheme for predicting breast cancer molecular subtypes.
- Source :
-
Medical & biological engineering & computing [Med Biol Eng Comput] 2024 May; Vol. 62 (5), pp. 1601-1613. Date of Electronic Publication: 2024 Feb 06. - Publication Year :
- 2024
-
Abstract
- Invasive gene expression profiling studies have exposed prognostically significant breast cancer subtypes: normal-like, luminal, HER-2 enriched, and basal-like, which is defined in large part by human epidermal growth factor receptor 2 (HER-2), progesterone receptor (PR), and estrogen receptor (ER). However, while dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been generally employed in the screening and therapy of breast cancer, there is a challenging problem to noninvasively predict breast cancer molecular subtypes, which have extremely low-data regimes. In this paper, a novel few-shot learning scheme, which combines lightweight contrastive convolutional neural network (LC-CNN) and multi-contrast learning strategy (MCLS), is worthwhile to be developed for predicting molecular subtype of breast cancer in DCE-MRI. Moreover, MCLS is designed to construct One-vs-Rest and One-vs-One classification tasks, which addresses inter-class similarity among normal-like, luminal, HER-2 enriched, and basal-like. Extensive experiments demonstrate the superiority of our proposed scheme over state-of-the-art methods. Furthermore, our scheme is able to achieve competitive results on few samples due to joint LC-CNN and MCLS for excavating contrastive correlations of a pair of DCE-MRI.<br /> (© 2024. International Federation for Medical and Biological Engineering.)
Details
- Language :
- English
- ISSN :
- 1741-0444
- Volume :
- 62
- Issue :
- 5
- Database :
- MEDLINE
- Journal :
- Medical & biological engineering & computing
- Publication Type :
- Academic Journal
- Accession number :
- 38316663
- Full Text :
- https://doi.org/10.1007/s11517-024-03031-0