Results 91 to 100 of about 118,357 (248)

OBUSight: Clinically Aligned Generative AI for Ophthalmic Ultrasound Interpretation and Diagnosis

Advanced Science, EarlyView.
OBUSight, a clinically aligned generative AI model that jointly generates reports and predicts diseases through multimodal semantic alignment, was trained and validated on a large multicenter dataset. OBUSight outperformed eight state‐of‐the‐art models, provided clinically reliable reports, enhanced diagnostic efficiency, and achieved performance ...
Xiaocong Liu, An Shao, Bingtao Guan, Ziyao Luo, Weiyi Lai, Xiaoling Huang, Jun Liu, Jie Yan, Huimin Li, Xiangji Pan, Jiawei Wang, Zichang Su, Yih Chung Tham, Jie Yang, Haotian Lin, Juan Ye, Hongxia Xu, Jian Wu   +17 more
wiley   +1 more source

CAPTCHA recognition based on deep convolutional neural network

Mathematical Biosciences and Engineering, 2019
Aiming at the problems of low efficiency and poor accuracy of traditional CAPTCHA recognition methods, we have proposed a more efficient way based on deep convolutional neural network (CNN).
Jing Wang, Jiaohua Qin, Xuyu Xiang, Yun Tan, Nan Pan   +4 more
doaj   +1 more source

Pathomics Signature for Prognosis and CA19‐9 Interception in Pancreatic Ductal Adenocarcinoma: A Real‐Life, Multi‐Center Study

Advanced Science, EarlyView.
This study develops a deep learning‐based pathomics model to predict survival outcomes in pancreatic cancer patients. The CrossFormer architecture analyzes routine H&E‐stained tissue slides, identifying key prognostic features including stromal patterns, cellular characteristics, and immune infiltration.
Qiangda Chen, Zhihang Xu, Yiping Zou, Zhenlai Jiang, Yecheng Li, Taochen He, Hanlin Yin, Jiali Li, Yanfei An, Jiande Han, Yuqi Xie, Wei Gan, Yaolin Xu, Wenquan Wang, Junyi He, Haibo Wang, Wenchuan Wu, Zhenyu Ye, Wenhui Lou, Jihui Hao, Liang Liu, Jun Yu, Ning Pu   +22 more
wiley   +1 more source

S3RL: Enhancing Spatial Single‐Cell Transcriptomics With Separable Representation Learning

Advanced Science, EarlyView.
Separable Spatial Representation Learning (S3RL) is introduced to enhance the reconstruction of spatial transcriptomic landscapes by disentangling spatial structure and gene expression semantics. By integrating multimodal inputs with graph‐based representation learning and hyperspherical prototype modeling, S3RL enables high‐fidelity spatial domain ...
Laiyi Fu, Penglei Wang, Gaoyuan Xu, Jitao Lu, Qinke Peng, Danyang Wu, Hequan Sun   +6 more
wiley   +1 more source

Utilization of ResNet Architecture and Transfer Learning Method in the Classification of Faces of Individuals with Down Syndrome

Journal of Applied Informatics and Computing
Classifying the faces of individuals with Down Syndrome poses a significant challenge in image processing and genetic anomaly detection. This study leverages the ResNet34 architecture and transfer learning methods to improve classification accuracy for ...
Made Doddy Adi Pranatha, Gede Herdian Setiawan, M Azman Maricar   +2 more
doaj   +1 more source

Affect State Classification from Face Segments Using Resnet-50 and SE-Resnet-50

International Journal of Innovative Technology and Exploring Engineering, 2020
One of the important components of an intelligent Human computer Interface system is accurate classification of the various affect states. Such interface systems are however plagued by a recurring problem of image occlusion. The challenge hence is to be able to classify the various affect states accurately from whatever portions of the face are ...
Dhananjay Theckedath*, R.R. Sedamkar
openaire   +1 more source

UniMR: A Plug‐and‐Play Framework of Automated Molecular Recognition for Scanning Tunneling Microscopy

Advanced Science, EarlyView.
UniMR, a training‐free framework for automated molecular recognition in STM images. By integrating adaptive feature selection with CLIP embeddings and Gaussian Mixture Modeling, UniMR achieves robust performance across diverse molecular systems and low‐resolution conditions.
Ziqiang Cao, Lingyin Zhang, Bingzheng Wu, Haonan Chen, Junhao Sun, Linghao Yan, Wenfei Li, Yangyang Wu, Zhifang Wang, Qigang Zhong, Lifeng Chi   +10 more
wiley   +1 more source

Classification of Lung Cancer with Convolutional Neural Network Method Using ResNet Architecture

Teknika
Lung cancer has become one of the most frightening specters in the world of health, leading many people to death each year. Therefore, the classification of lung cancer types is very important to determine the appropriate treatment steps.
Aldrich Deril Christian Zebua, Dedy Yehezkiel Marbun, Felix Thedora, Mawaddah Harahap   +3 more
doaj   +1 more source

Convergence Theory of Learning Over-parameterized ResNet: A Full Characterization

, 2019
ResNet structure has achieved great empirical success since its debut. Recent work established the convergence of learning over-parameterized ResNet with a scaling factor $\tau=1/L$ on the residual branch where $L$ is the network depth.
Chen, Wei, Liu, Tie-Yan, Yi, Mingyang, Yu, Da, Zhang, Huishuai   +4 more
core  

Poincar\'e ResNet

, 2023
This paper introduces an end-to-end residual network that operates entirely on the Poincar\'e ball model of hyperbolic space. Hyperbolic learning has recently shown great potential for visual understanding, but is currently only performed in the penultimate layer(s) of deep networks.
van Spengler, Max, Berkhout, Erwin, Mettes, Pascal   +2 more
openaire   +1 more source