Results 251 to 260 of about 549,786 (309)

Advancing Self-Supervised Learning for Building Change Detection and Damage Assessment: Unified Denoising Autoencoder and Contrastive Learning Framework [PDF]

Songxi Yang, Bo Peng, Tang Sui, Meiliu Wu, Qunying Huang   +4 more
openalex   +1 more source

Mass‐Produced and High‐Performance Nanowell Biosensor Fabricated via Semiconductor Manufacturing for Rapid and Accurate COVID‐19 Diagnosis in the Clinical Field

Advanced Science, EarlyView.
A semiconductor‐fabricated nanowell biosensor enables rapid, scalable, and highly reproducible detection of SARS‐CoV‐2 antigens from nasal swabs within ∼10 minutes. Clinical validation in 249 retrospective and 243 prospective patient samples demonstrates high sensitivity and specificity, minimal cross‐reactivity, and robust batch‐to‐batch ...
Yoo Min Park, Zahra Rezaei, Nam Ho Bae, Donggee Rho, Da‐Seul Kim, EunYoung Go, YoungTae Seo, Seok Jae Lee, Won Chan Seo, Luke P. Lee, HeaYeon Lee, Su Ryon Shin   +11 more
wiley   +1 more source

Unbiased self supervised learning of kidney histology reveals phenotypic and prognostic insights. [PDF]

Sci Rep
Pandit K, Coudray N, Quiros AC, Surapaneni A, Upadhyay D, Vanguri RS, Hirohama D, Mohandes S, Schlosser P, Thiessen-Philbrook H, Wen Y, Parikh CR, Rhee EP, Waikar SS, Schmidt I, Rosenberg AZ, Palmer MB, Susztak K, Grams ME, Tsirigos A, TRIDENT Study Investigators.   +20 more
europepmc   +1 more source

Implicit sensing self-supervised learning based on graph multi-pretext tasks for traffic flow prediction [PDF]

Ali Reza Sattarzadeh, Pubudu N. Pathirana, Marimuthu Palaniswami   +2 more
openalex   +1 more source

Multimodal Wearable Biosensing Meets Multidomain AI: A Pathway to Decentralized Healthcare

Advanced Science, EarlyView.
Multimodal biosensing meets multidomain AI. Wearable biosensors capture complementary biochemical and physiological signals, while cross‐device, population‐aware learning aligns noisy, heterogeneous streams. This Review distills key sensing modalities, fusion and calibration strategies, and privacy‐preserving deployment pathways that transform ...
Chenshu Liu, Haolin Fan, Minwoo Kim, Tong Zhou, Pinyi Yang, Lingdi Zhao, Yiran Wang, Ziyuan Che, Chia‐Wei Liu, Bingbing Li, Yangzhi Zhu   +10 more
wiley   +1 more source

EVA-X: a foundation model for general chest x-ray analysis with self-supervised learning. [PDF]

NPJ Digit Med
Yao J, Wang X, Song Y, Zhao H, Ma J, Chen Y, Liu W, Wang B.   +7 more
europepmc   +1 more source

Magneto‐X Effects in Magnetic Soft Materials and Their Applications

Advanced Science, EarlyView.
This review systematically explores magnetic soft materials (MSMs), a novel class of composites that transform under magnetic fields. It catalogs fundamental “Magneto‐X” effects, classifies materials by their matrix and magnetic fillers, and highlights transformative applications in soft robotics, biomedical devices, flexible electronics, etc. Finally,
Ziyin Xiang, Xiangling Xia, Benjamin Ducharne, Yujie Ye, Jie Shang, Run‐Wei Li   +5 more
wiley   +1 more source

A foundation transformer model with self-supervised learning for ECG-based assessment of cardiac and coronary function. [PDF]

NEJM AI
Moody JB, Poitrasson-Rivière A, Renaud JM, Hagio T, Alahdab F, Al-Mallah MH, Vanderver MD, Goonewardena SN, Ficaro EP, Murthy VL.   +9 more
europepmc   +1 more source

Soft Robotics and Advanced Technologies for Minimally Invasive Bioprinting: The Future of Internal Organ Repair

Advanced Science, EarlyView.
This review examines the evolution of bioprinting toward minimally invasive in situ strategies for internal organ regeneration. It defines the technological roadmap from handheld systems to advanced minimally invasive bioprinting platforms, positioning soft robotics as a core enabler.
Duc Tu Vu, Nhu An Phan, Sy Trung Ngo, Minh Tri Phan, Thanh‐An Truong, Chi Cong Nguyen, Phuoc Thien Phan, Hoang‐Phuong Phan, Thanh Nho Do, Mai Thanh Thai   +9 more
wiley   +1 more source

Privacy-Aware Continual Self-Supervised Learning on Multi-Window Chest Computed Tomography for Domain-Shift Robustness. [PDF]

Bioengineering (Basel)
Tasai R, Li G, Togo R, Ogawa T, Hirata K, Tang M, Yoshimura T, Sugimori H, Nishioka N, Shimizu Y, Kudo K, Haseyama M.   +11 more
europepmc   +1 more source