1. Automated classification of elliptical cancer cells with stain-free holographic imaging and self-supervised learning.
- Author
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Rehman, Abdur, An, Hyunbin, Park, Seonghwan, and Moon, Inkyu
- Subjects
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CONVOLUTIONAL neural networks , *CANCER cells , *TUMOR classification , *BREAST , *SUPERVISED learning , *IMAGE recognition (Computer vision) , *HOLOGRAPHY , *DIGITAL holographic microscopy - Abstract
• We propose deep learning models for classification of cancer cells belonging to different organs on holographic images. • We present self-supervised pretraining and hyperparameter tuning for efficient training on biomedical holographic datasets. • Perform extensive experiments with self-supervised frameworks and compared self-supervised learning with supervised learning and proved the effectiveness of the former for holographic images classification. • Proposed work can be an effective tool for the automatic classification of cancer cells for medical diagnosis using self-supervised learning. Image-based stain-free elliptical cancer cell classification is challenging, due to the inter-class morphological similarity. In this paper, we address the classification of different types of cancer cell lines (lung, breast, bladder, and skin) by utilizing self-supervised learning, and compare it with supervised learning based on convolutional neural network. Digital holography in a microscopic configuration was used to obtain stain-free quantitative phase images representing the intracellular content and morphology of cells. The performance of self-supervised learning in natural images shows promising results, and consistently closes the gap between self-supervised and supervised learning. The ability of self-supervised learning to effectively utilize unlabeled data for training is instrumental in the biomedical domain, where labeled data is scarce. Our goal is to study different self-supervised frameworks of biomedical holographic data, and determine how they can be utilized to advance liquid biopsy for the detection of cancer cells. After extensive experimentation, we conclude that self-supervised learning improves the classification performance on cancer cell datasets, and outperforms supervised learning when training data is limited, which is mostly the case in biomedical imaging. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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