Results 181 to 190 of about 52,085 (312)

Few-shot learning for the classification of colorectal neuroendocrine tumors and polyps on endoscopic images. [PDF]

J Int Med Res
Zhu S, Ge H, Wang Y, Lin J, Zhang R, Yin M, Zhu J, Chao C.   +7 more
europepmc   +1 more source

Data‐Driven Feedback Identifies Focused Ultrasound Exposure Regimens for Improved Nanotheranostic Targeting of the Brain

Advanced Science, EarlyView.
ABSTRACT The blood‐brain barrier (BBB) renders the delivery of nanomedicine in the brain ineffective and the detection of circulating disease‐related DNA from the brain unreliable. Here, we demonstrate that microbubble‐enhanced focused ultrasound (MB‐FUS) mediated BBB opening, supported by large‐data models predict sonication regimens for safe and ...
Hohyun Lee, Victor Menezes, Shiqin Zeng, Chulyong Kim, Cynthia M. Baseman, Jae Hyun Kim, Samhita Padmanabhan, Pranav Premdas, Naima Djeddar, Anton Bryksin, Nikhil Pandey, Pavlos Anastasiadis, Anthony J. Kim, Tobey J. MacDonald, Chetan Bettegowda, Graeme F. Woodworth, Felix J. Herrmann, Costas Arvanitis   +17 more
wiley   +1 more source

An Ensemble Learning for Automatic Stroke Lesion Segmentation Using Compressive Sensing and Multi-Resolution U-Net. [PDF]

Biomimetics (Basel)
Emami M, Tinati MA, Musevi Niya J, Danishvar S.   +3 more
europepmc   +1 more source

Euclid: Searches for strong gravitational lenses using convolutional neural nets in Early Release Observations of the Perseus field [PDF]

R Pearce-Casey, B.C Nagam, de Wilde Jaap, Valerio Busillo, L. Ulivi, I.T Andika, Anna G. Manjón, L. Leuzzi, Predrag Matavulj, S. Serjeant, M. Walmsley, A. Hornstrup, C. M. O’Riordan, B. Clément, C. Tortora, T. E. Collett, F. Courbin, R. Gavazzi, R. B. Metcalf, R. Cabanac, H. M. Courtois, J. Crook-Mansour, L. Delchambre, G. Despali, L. R. Ecker, A. Franco, P. Holloway, K. Jahnke, G. Mahler, L. Marchetti, A. Melo, M. Meneghetti, O. Müller, A. A. Nucita, J. Pearson, K. Rojas, C. Scarlata, S. Schuldt, D. Sluse, S. H. Suyu, M. Vaccari, S. Vegetti, A. Verma, G. Vernardos, M. Bolzonella, M. Kluge, T. Saifollahi, M. Schirmer, C. Stone, A. Paulino-Afonso, L. Bazzanini, N. B. Hogg, L. V. E. Koopmans, S. Kruk, F. Mannucci, J. M. Bromley, A. Díaz-Sánchez, H. J. Dickinson, D. M. Powell, H. Bouy, R. Laureijs, B. Altieri, A. Amara, S. Andreon, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, P. Battaglia, D. Bonino, E. Branchini, M. Brescia, J. Brinchmann, A. Caillat, S. Camera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, M. Castellano, G. Castignani, S. Cavuoti, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, M. Cropper, A. Da Silva, H. Degaudenzi, G. De Lucia, A. M. Di Giorgio, J. Dinis, F. Dubath, X. Dupac, S. Dusini, M. Farina, S. Farrens, F. Faustini   +99 more
openalex   +1 more source

Modulus Matching and Interface Enhancement: A Synergistic Strategy for Antidelamination High‐Performance Stretchable Triboelectric Nanogenerator

Advanced Science, EarlyView.
A bioinspired TENG features a homologously paired interface with matched modulus. Ultrasonic cavitation enhances its toughness to 190 N m−1 (3.2× higher), preventing delamination under 400% strain. Abstract With the growing demand for flexible self‐powered energy sources for wearable bioelectronics, triboelectric nanogenerator (TENG) have emerged as a ...
Shiwei Xu, Pengfan Wu, Feng Qin, Endian Cui, Zhongyong Mo, Hengyu Guo, Xiaojing Mu, Hua Yu   +7 more
wiley   +1 more source

Iterative reconstruction of industrial positron images with generative networks. [PDF]

PLoS One
Zhu M, Zhao M, Yao M.
europepmc   +1 more source

SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud [PDF]

, 2017
BoRui Wu, Alvin Wan, Xiangyu Yue, Kurt Keutzer   +3 more
openalex   +1 more source

Inferring Gene Regulatory Networks From Single‐Cell RNA Sequencing Data by Dual‐Role Graph Contrastive Learning

Advanced Science, EarlyView.
RegGAIN is a novel and powerful deep learning framework for inferring gene regulatory networks (GRNs) from single‐cell RNA sequencing data. By integrating self‐supervised contrastive learning with dual‐role gene representations, it consistently outperforms existing methods in both accuracy and robustness.
Qiyuan Guan, Jiating Yu, Jieyi Pan, Fan Yuan, Jiadong Ji, Rusong Zhao, Zhi‐Ping Liu, Bingqiang Liu, Ling‐Yun Wu, Duanchen Sun   +9 more
wiley   +1 more source

Liver tumor segmentation CT data based on Alexnet-like convolution neural nets

, 2016
Alexandr N. Korabelnikov, А. В. Колсанов, Sergey S. Сhaplygin, P. M. Zelter, K. Bychenkov, Артем Никоноров   +5 more
openalex   +2 more sources

Matrix-based vector representations in neural networks for classifying molecular biology data. [PDF]

Bioinform Adv
Nanni L, Brahnam S, Fusaro D.
europepmc   +1 more source