Researchers have designed an energy-aware deep learning framework combining radial attention, residual learning, and ...

A novel sectional deep learning framework with region normalization improves hepatocellular carcinoma detection and staging ...

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In the era of data-driven medicine, biomedical imaging has evolved from a purely diagnostic tool to a cornerstone of precision healthcare. The confluence of deep learning (DL) and biomedical image ...

From an architect designing a building to a biologist trying to dissect the molecular causes of a disease, it is crucial to understand the relationship between structure and function. At the scale of ...

Deep learning uses multi-layered neural networks that learn from data through predictions, error correction and parameter adjustments. It started with the ...

Accurate seed counting is an essential task in agricultural research and farming, supporting activities such as crop breeding, yield prediction, and weed management. Traditional manual seed counting, ...

This Collection calls for submissions of original research into techniques that facilitate the advancement of deep learning for image analysis and object detection, driving computer vision forward and ...

Abstract: Breast cancer stands as a predominant health concern for women globally. As mammography is the primary screening tool for breast cancer detection, improving the detection of breast cancer at ...