Deep learning models show promise for label-efficient cancer diagnosis

This research paper explores three learning environments—supervised, semi-supervised, and self-supervised learning—for efficient cancer diagnosis using deep learning models. The study evaluated Residual Network-50, Visual Geometry Group-16, and EfficientNetB0 on datasets for kidney, lung, and breast cancer. Findings indicate that semi-supervised learning offers a viable alternative to traditional supervised learning, especially when labeled data is scarce, achieving comparable results with less computational cost and fewer labeled samples. AI

IMPACT Semi-supervised learning shows potential for improving cancer diagnosis efficiency, especially in data-limited scenarios.

RANK_REASON The cluster contains a withdrawn academic paper discussing machine learning methods for medical diagnosis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• breast cancer
• EfficientNetB0
• kidney cancer
• lung cancer
• Residual Network-50
• self-supervised learning
• semi-supervised learning
• supervised learning
• Visual Geometry Group-16

• paper
• other

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