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AI trained on sleep data predicts future disease and mortality years in advance

The SleepFM model reveals how sleep analysis can predict disease risk, offering insights into sleep's role as a vital health ...

WiMi Releases Next-Generation Quantum Convolutional Neural Network Technology for Multi-Channel Supervised Learning

WiMi Releases Next-Generation Quantum Convolutional Neural Network Technology for Multi-Channel Supervised Learning BEIJING, Jan. 05, 2026––WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the ...
IEEE

Noisier2Inverse: Self-Supervised Learning for Image Reconstruction With Correlated Noise

Abstract: We propose Noisier2Inverse, a correction-free, self-supervised deep learning method for general inverse problems. Our approach learns a reconstruction function without requiring ground truth ...
BMJ Open

Development and evaluation of a diagnostic aiding tool for differentiating tropical fevers using artificial intelligence approach: a study protocol from tertiary care hospital ...

Introduction Application of artificial intelligence (AI) tools in the healthcare setting gains importance especially in the domain of disease diagnosis. Numerous studies have tried to explore AI in ...
IEEE

SSL-MBC: Self-Supervised Learning With Multibranch Consistency for Few-Shot PolSAR Image Classification

Abstract: Deep learning methods have recently made substantial advances in polarimetric synthetic aperture radar (PolSAR) image classification. However, supervised training relying on massive labeled ...