Results 81 to 90 of about 8,876 (216)

Comparison of AR between the Swin-cryoEM model and Swin Transformer model.

Comparison of AR between the Swin-cryoEM model and Swin Transformer model.
JinLing Wang (18334331), QingFeng Chen (18334334), Jiachi Shi (18334340), Zhuomin Xu (18334343), Xian Feng (1545901), Kun Fang (619812), YouMing Qu (18334337)   +6 more
core   +1 more source

Visualizing Image Segmentation Network Behavior Through the Lens of Scale Space Analysis

Computer Graphics Forum, EarlyView.
Abstract Deep neural networks are widely used for image segmentation, also in sensitive applications such as medical imaging or autonomous driving. However, few explainable AI methods are available that help developers understand such networks beyond classification.
A. C. Mikliss, T. Schultz
wiley   +1 more source

Multi‐angle, cross‐domain fusion strategy enhances automated insect identification and hierarchical categorization: a case study on assassin bugs (Hemiptera: Reduviidae)

Cladistics, EarlyView.
Abstract Automated insect identification systems hold significant value for biodiversity monitoring, pest management, citizen science initiatives and systematic studies, particularly in an era of declining expertise in insect taxonomy. However, current deep learning approaches often rely on standardized specimen photos from limited‐angles and ...
Xinkai Wang, Huaiyu Liu, Zhuo Chen, Yisheng Zhao, Yingqi Liu, Haoyang Xiong, Yuange Duan, Fang Song, Wanzhi Cai, Xuankun Li, Hu Li   +10 more
wiley   +1 more source

Frequency Domain and Gradient-Spatial Multi-Scale Swin KANsformer for Remote Sensing Scene Classification

Remote Sensing
Transformer-based deep learning techniques have recently shown outstanding potential in remote sensing scene classification (RSSC), benefiting from their ability to capture global semantic relationships and contextual dependencies.
Xiaozhang Zhu, Junqing Huang, Haihui Wang   +2 more
doaj   +1 more source

A Unified Deep Learning Framework for Instance Segmentation Across Diverse Cytological Stains

Cytopathology, EarlyView.
Transformer‐based unified cytology segmentation across Papanicolaou, Feulgen and AgNOR achieves stain‐invariant performance. Mask2Former maximises boundary precision (AP75) on the combined dataset, enabling one model to replace multiple stain‐specific deployments without accuracy loss while simplifying clinical integration.
Luís Otávio Santos, Rodrigo de Paula E. Silva Ribeiro, Bibiana Quatrin Tiellet, Laura Maria Effting Silva, Fabiana Botelho de Miranda Onofre, Alexandre Sherlley Casimiro Onofre, Aldo von Wangenheim   +6 more
wiley   +1 more source

Deep Learning Models for Detection of Periapical Radiolucent Lesions on Panoramic Radiographs: A Systematic Review and Meta‐Analysis

International Endodontic Journal, EarlyView.
ABSTRACT Background Panoramic radiographs are used routinely to screen dental conditions and treatment patterns. Recently, numerous studies have suggested that deep learning (DL) models can be utilized for analysing panoramic radiographs. Objective This review aimed to evaluate the accuracy of DL models in detecting periapical radiolucent lesions (PRLs)
Ibrahim Ali Ahmad, Raidan Ba‐Hattab, Sadeq Ali Al‐Maweri, Faleh Tamimi   +3 more
wiley   +1 more source

Multi-scale capsule Swin Transformer-based method for SAR image target recognition

Tongxin xuebao
A multi-scale capsule Swin Transformer network (MSCSTN) was proposed by synergizing the semantic feature encoding of capsule units with the context feature mapping of Swin Transformer. Capsule encoding and the Swin Transformer were jointly applied to SAR
HOU Yuchao, WANG Jie, LI Hongtao, HAO Yan, DUAN Xiaoqi, HUANG Kaiwen, TIAN Youliang   +6 more
doaj  

FML-Swin: An Improved Swin Transformer Segmentor for Remote Sensing Images

IEEE Access
Semantic segmentation of urban remote sensing images is a very challenging task. Due to the complex background, occlusion overlap and small scale target of urban remote sensing image, the semantic segmentation results have some defects such as target confusion and similarity, target boundary ambiguity, and small scale target omission.
Tianren Wu, Wenqin Deng, Rui Lin, Junzhe Jiang 0004, Xueyun Chen   +4 more
openaire   +2 more sources

Application of Noise2Inverse and adaptation (Noise2Phase) to single‐mask x‐ray phase contrast micro‐computed tomography

Journal of Microscopy, EarlyView.
Abstract X‐ray phase contrast imaging (XPCI), when implemented in micro‐computed tomography (micro‐CT) mode, offers high‐contrast 3D imaging of weakly‐attenuating material samples. In the so‐called single‐mask edge illumination approach, a mask with periodically spaced transmitting apertures is used to split the x‐ray beam into narrow beamlets; when ...
Khushal Shah, Grammatiki Lioliou, Dai Chen, Alexander Denker, Peter Munro, Marco Endrizzi, Alberto Astolfo, Alessandro Olivo, Charlotte K. Hagen   +8 more
wiley   +1 more source

Advanced deepfake detection leveraging swin transformer technology

The widespread use of deepfake technology in recent years has made it extremely difficult to differentiate between real and fake images, usually AI-generated images.
Gourisaria, Mahendra Kumar, Edalatpanah, Seyed Ahmad, Mishra, Soumya Ranjan, Mohapatra, Hitesh   +3 more
core   +2 more sources