Results 91 to 100 of about 15,271 (229)

Deep Learning Empowered Microstructure Codebook: New Paradigm for Multi‐Parameter Tissue Characterization Estimation

Human Brain Mapping, Volume 47, Issue 5, 1 April 2026.
We propose DEMIC, a deep‐learning microstructure codebook framework for dMRI microstructure imaging: (1) accurate multi‐parameter estimation from undersampled data; (2) robust cross‐protocol and cross‐model generalization; and (3) flexible transfer to new microstructural indices via fine‐tuning.
Tenglong Wang, Zhonghua Wan, Shuxin Cao, Jiahao Yu, Yifei He, Yu Xie, Fan Zhang, Ye Wu   +7 more
wiley   +1 more source

Predicting ventilation from single breathing phase non‐contrast CT using Swin Transformers

Medical Physics, Volume 53, Issue 4, April 2026.
Abstract Background Pulmonary ventilation imaging enables functional avoidance radiotherapy treatment plans by quantifying regional lung function. However, current clinical standards, such as 99𝑚Tc‐based single‐photon emission computed tomography (SPECT), rely on radioactive tracers, which can introduce imaging deposition artifacts.
Yi‐Kuan Liu, Hsu‐Ting Kuo, Alaa Melek, Richard Castillo, Yevgeniy Vinogradskiy, Lili Zhao, Girish Nair, Edward Castillo   +7 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  

Pavement Crack Detection Based on the Improved Swin-Unet Model

Buildings
Accurate pavement surface crack detection is crucial for analyzing pavement survey data and the development of maintenance strategies. On the basis of Swin-Unet, this study develops the improved Swin-Unet (iSwin-Unet) model with the developed skip ...
Song Chen, Zhixuan Feng, Guangqing Xiao, Xilong Chen, Chuxiang Gao, Mingming Zhao, Huayang Yu   +6 more
doaj   +1 more source

Automated Glaucoma Detection Using Vision and Swin Transformers: Advancing Ophthalmic AI [PDF]

Purpose:Glaucoma is one of the most common causes of permanent blindness in the world; early detection and precise diagnosis are essential to successful treatment.Convolutional Neural Networks (CNNs) are one of the deep learning techniques that have ...
Gireesh, Dr.N., Sakunthala, D.
core   +2 more sources

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, Xueyun Chen   +4 more
openaire   +2 more sources

Deep Learning‐Based Inner Ear Subregion Segmentation in 3D T2‐Weighted MRI Using Label‐Preserving Data Augmentation

NMR in Biomedicine, Volume 39, Issue 4, April 2026.
A deep learning segmentation model was proposed for automated inner ear subregion segmentation using 3D T2‐weighted MRI. A transformer‐based model with label‐preserving data augmentation improves delineation of thin and complex structures such as the semicircular canals.
Wooseung Kim, Yeonah Kang, Seokhwan Lee, Ho‐Joon Lee, Yoonho Nam   +4 more
wiley   +1 more source

Off-line identifying Script Writers by Swin Transformers and ResNeSt-50

ELCVIA Electronic Letters on Computer Vision and Image Analysis
In this work, we present two advanced models for identifying script writers, leveraging the power of deep learning. The proposed systems utilize the new vision Swin Transformer and ResNeSt-50.
Afef Kacem Echi, Takwa Ben Aïcha Gader
doaj   +1 more source

Classifying Deepfakes Using Swin Transformers

3 ...
Xi, Aprille J., Chen, Eason
openaire   +2 more sources

Supervised Swin Transformer‐Based Predictive Lithological Mapping and Uncertainty Quantification Using Aeromagnetic and Gravity Data

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 2, April 2026.
Abstract Lithological mapping is essential for the exploration of critical minerals supporting energy transition and national defense. Although recent advancements have incorporated multi‐source data sets and leveraged machine learning and deep learning (DL) methods, lithological mapping continues to face significant challenges, such as data imbalance,
Liang Ding, Gilles Bellefleur, Olivier Boulanger, Phuong Vo   +3 more
wiley   +1 more source