Results 181 to 190 of about 11,006,319 (257)

Dissecting self-supervised learning strategies for transfer learning in MRI prostate cancer diagnosis. [PDF]

Sci Rep
Facuse J, Campanini D, Parra D, Besa C, Salas R, Estévez PA, Mery D.   +6 more
europepmc   +1 more source

Neuromorphic Devices and Computing for Sensing, Memory, and Control

Advanced Science, EarlyView.
This review introduces neuromorphic devices made from diverse materials. These devices mimic neuronal functions and architectures and, when integrated with artificial or biological computing, can form closed loops with neurons for pressure, optical, acoustic, and biochemical sensing and modulation.
Zhengguang Zhu, Nicholas Schaffer, Xiao Yang   +2 more
wiley   +1 more source

Patlak-Guided Self-Supervised Learning for Dynamic PET Denoising. [PDF]

IEEE Trans Radiat Plasma Med Sci
Liu Q, Guo X, Tsai YJ, Gallezot JD, Chen MK, Guo L, Xie H, Panin V, Carson RE, Liu C.   +9 more
europepmc   +1 more source

Computationally Evidence‐Grounded Sequence‐First Design of Peptide Binders

Advanced Science, EarlyView.
BOND‐PEP enables controllable, sequence‐first peptide binder design by grounding generation in binding evidence retrieved for each target. It uses topology‐conditioned message passing to integrate relevant peptide examples with the target protein sequence, forming a residue‐level representation that guides the generation of diverse, target‐specific ...
Wenze Ding
wiley   +1 more source

Multimodal Nomogram for the Prenatal Risk Assessment of Hypoplastic Left Heart Syndrome Using Self-Supervised Learning. [PDF]

J Ultrasound Med
Wu X, Cheng C, Ran Z, Zhu H, Xu G, Wu T, Zhao S.   +6 more
europepmc   +1 more source

Learning Work Function via Implicit Reasoning on Electrostatic Potential Landscapes

Advanced Science, EarlyView.
StructPot‐CLR establishes a cross‐modal contrastive learning framework that aligns the crystal structures of 2D materials with plane‐averaged electrostatic potential landscapes for physically informed work‐function prediction. The model achieves an MAE of 0.265 eV and an R2 of 0.902 on the held‐out test set while accurately preserving key morphological
Haoyu Wan, Yue Wu, Tianhao Su, Deng Pan
wiley   +1 more source

Stain-adaptive self-supervised learning for histopathology image analysis

Stain-adaptive self-supervised learning for histopathology image ...
Yuan-yuan Yang (20798033), Da-Han Wang (20777435), Xu-Yao Zhang (20798036), Haili Ye (20468045), Xin Yang (104842), Heguang Huang (5556221), Shunzhi Zhu (20777438)   +6 more
core  

Explainable hybrid CNN-transformer with self-supervised learning for structural analysis of paranasal sinus CT. [PDF]

Front Comput Neurosci
Ullah N, Algamdi SA, Sadad T.
europepmc   +1 more source

The CXCL5/CXCR2 Axis Attenuates Ferroptosis in Intrahepatic Cholangiocarcinoma Through the Positive Feedback Loop Between PTGS2 Transcriptional Activation and Neutrophil Recruitment

Advanced Science, EarlyView.
The CXCL5/CXCR2 axis directly inhibits ferroptosis in ICC cells by activating the NF‐κB/PTGS2 pathway. Meanwhile, the CXCL5/CXCR2 axis recruits N2 TANs, and the activated PTGS2 promotes the secretion of TNF‐α from the recruited N2 TANs via the CCL2/CCL7/CCR2 pathway, thereby activating the NF‐κB/PTGS2 axis and forming a positive feedback loop, which ...
Chanqi Ye, Kefeng Lin, Shuyao Yang, Yan Zhang, Wenli Gao, Jiaqi Chen, Ying Dong, Rui Bai, Yang Tian, Minghao Wang, Zhijun Yuan, Yuxiao Ma, Yuqin He, Ruyin Chen, Wenguang Fu, Qi Jiang, Qiong Li, Jian Ruan   +17 more
wiley   +1 more source

Self-supervised learning can distinguish myelodysplastic neoplasms from clinical mimics using bone marrow biopsies. [PDF]

Blood Neoplasia
Mehrtash V, Le H, Jafarzadeh B, Sharaf M, Flaifel A, Huang X, Ward N, Hasserjian R, Loghavi S, Garcia-Manero G, Tsirigos A, Park CY.   +11 more
europepmc   +1 more source