Results 201 to 210 of about 5,739,313 (302)

Exploring a Novel Conv‐Transformer Network for Multi‐Modality Heart Segmentation

iRADIOLOGY, EarlyView.
We propose SFAM‐TransUnet for multimodality whole heart segmentation, a novel deep learning framework combining CNNs and transformers. Extensive experiments conducted on the clinical Multi‐Modality Whole Heart Segmentation datasets demonstrate that SFAM‐TransUnet outperforms various alternative methods.
Youyou Ding, Hao Dang, Jiayi Luo, Xiaoyu Zhuo, Ningyu Huang, Junsheng Xiao, Zongwang Lv   +6 more
wiley   +1 more source

Region-guided focal adversarial learning for CT-to-MRI translation: A proof-of-concept and validation study in hepatocellular carcinoma. [PDF]

Med Phys
Xia YF, Zeng M, Sun SW, Liu QP, Zhang JL, Zhi R, Lu FY, Chen W, Zhang YD.   +8 more
europepmc   +1 more source

Adversarial reinforcement learning

Vulnerabilities in neural networks compromise the robustness of Machine Learning (ML) algorithms. Minimal input noise can hinder the performance of Deep Reinforcement Learning (DRL) algorithms. Therefore, it is crucial to understand these vulnerabilities to develop defense mechanisms that enhance the resilience of RL agents.
openaire   +2 more sources

Virtual Magnetic Resonance Elastography Using a Deep Generative Model for Liver Fibrosis Staging

iRADIOLOGY, EarlyView.
The proposed Registration‐based Generative Adversarial Network‐Convolutional Block Attention Module (RegGAN‐CBAM) model efficiently and reliably generates virtual MR elastography using diffusion weighted imaging. Strong correlation between virtual and native MR elastography for liver stiffness and viscosity are observed.
Longyu Sun, Yikun Wang, Yan Li, Xumei Hu, Wenyue Mao, Mengting Sun, Zian Wang, Fuhua Yan, Ruokun Li, Chengyan Wang   +9 more
wiley   +1 more source

VariGAN: Enhancing Image Style Transfer via UNet Generator, Depthwise Discriminator, and LPIPS Loss in Adversarial Learning Framework. [PDF]

Sensors (Basel)
Guan D, Lin X, Zhang H, Zhou H.
europepmc   +1 more source

Deep Learning Integration in Optical Microscopy: Advancements and Applications

Microscopy Research and Technique, EarlyView.
It explores the integration of DL into optical microscopy, focusing on key applications including image classification, segmentation, and computational reconstruction. ABSTRACT Optical microscopy is a cornerstone imaging technique in biomedical research, enabling visualization of subcellular structures beyond the resolution limit of the human eye ...
Pottumarthy Venkata Lahari, Sagnika Dutta, H. Deeksha, Samreen A. Patel, Budheswar Dehury, Nirmal Mazumder   +5 more
wiley   +1 more source

Stepwise incremental pretraining for integrating discriminative, restorative, and adversarial learning. [PDF]

Med Image Anal
Guo Z, Islam NU, Gotway MB, Liang J.
europepmc   +1 more source

Artificial intelligence strategies for predicting kinase inhibitor resistance: A comprehensive review of methods, challenges, and future perspectives

Journal of Intelligent Medicine, EarlyView.
Abstract Kinase inhibitors are essential in targeted cancer therapy, yet resistance often emerges through secondary mutations, activation of compensatory signaling pathways, or drug‐efflux mechanisms. Artificial intelligence (AI) provides a workflow‐based strategy rather than a list of unrelated tools for predicting and addressing kinase‐inhibitor ...
Faris Hassan, Mohanad Ali Deifallah, Alaa Zaghloul, Rania Elgohary   +3 more
wiley   +1 more source

A medical image classification method based on self-regularized adversarial learning. [PDF]

Med Phys
Fan Z, Zhang X, Ruan S, Thorstad W, Gay H, Song P, Wang X, Li H.   +7 more
europepmc   +1 more source

Preparing for Tomorrow's Teamwork: Insights From eSports on How Human Expertise Shapes Training Needs for AI‐Integrated Work

Journal of Organizational Behavior, EarlyView.
ABSTRACT As organizations increasingly adopt human‐AI teams (HATs), understanding how to enhance team performance is paramount. A crucially underexplored area for supporting HATs is training, particularly helping human teammates to work with these inorganic counterparts.
Caitlin M. Lancaster, Christopher Flathmann, Jennifer Hsu, Nathan J. McNeese, Thomas A. O'Neill, Eduardo Salas   +5 more
wiley   +1 more source