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13 June 2024 FSC-CLLDA: few-shot medical image classification based on contrastive learning and LDA
Qinglei Guo, Wenjing Zhang, Tao Luo, Jianfeng Li
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Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131801J (2024) https://doi.org/10.1117/12.3033659
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China
Abstract
Deep learning (DL) techniques have been widely applied in medical image analysis. In particular, the DL-based medical image classification has been adequately investigated on large-size annotated datasets. However, it is cost-expensive to collect a large amount of high-quality and large-scale annotated medical images. Our proposal is addressing this problem by a few-shot medical image classification method that uses contrastive learning and linear discriminant analysis (FSCCLLDA). A well-performing encoder is pre-trained using contrastive learning to extract more extensive semantic information that is unrelated to the label. Moreover, the features are transformed into low-dimensional space using linear discriminant analysis (LDA). The transformed features are similar within each class and discriminatory among classes. Experiments on ISIC2018 and BreakHis datasets show that the proposed FSC-CLLDA algorithm outperforms the compared baselines in accuracy.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Qinglei Guo, Wenjing Zhang, Tao Luo, and Jianfeng Li "FSC-CLLDA: few-shot medical image classification based on contrastive learning and LDA", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131801J (13 June 2024); https://doi.org/10.1117/12.3033659
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KEYWORDS
Image classification
Medical imaging
Classification systems
Feature extraction
Machine vision
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