Results 51 to 60 of about 206,029 (232)

Transformer‐Based PET/CT Fusion Enables Preoperative Prediction and Prognostic Stratification of Spread Through Air Spaces in Lung Adenocarcinoma

Advanced Science, EarlyView.
An eight‐stream 2.5D Transformer integrates complementary PET metabolic and CT morphologic information to estimate spread‐through‐air‐spaces risk before surgery in lung adenocarcinoma. The model generalizes across independent centers, while its continuous risk score independently stratifies progression‐free survival, supporting further prospective ...
Xin‐Yu Zhu, Meng Meng, Zhen‐Zhen Wang, Deng‐Lu Lu, Ya‐Min Wei, Xu‐Chang Mi, Xing‐Yu Mu, Wei Fu   +7 more
wiley   +1 more source

Foundation Model‐Enabled Pathology‐Informed MRI Model for Preoperative Prediction of Vessels Encapsulating Tumor Clusters in Resectable Hepatocellular Carcinoma: A Multicenter Retrospective Study

Advanced Science, EarlyView.
PIVOT uses pathology‐derived morphologic and vascular representations from postoperative H&E and CD34 whole‐slide images to guide model development, while clinical inference requires only preoperative multiparametric MRI. Across five centers, PIVOT enabled accurate preoperative assessment of vessels encapsulating tumor clusters, stratified patients by ...
Zhehan Shen, Wanmin Liu, Xiaoming Li, Wenjie Zou, Yiman Li, Jie Yuan, Yasen Yimit, Shihang Wang, Linhao Qu, Hui Dong, Wei Chen, Ningyang Jia, Peijun Wang   +12 more
wiley   +1 more source

Deep Reinforcement Learning with Swin Transformers

Proceedings of the 2024 8th International Conference on Digital Signal Processing
Transformers are neural network models that utilize multiple layers of self-attention heads and have exhibited enormous potential in natural language processing tasks. Meanwhile, there have been efforts to adapt transformers to visual tasks of machine learning, including Vision Transformers and Swin Transformers.
Li Meng, Morten Goodwin, Anis Yazidi, Paal Engelstad   +3 more
openaire   +3 more sources

Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

, 2022
13 pages, 3 ...
Ali Hatamizadeh, Vishwesh Nath, Yucheng Tang, Dong Yang 0005, Holger R. Roth, Daguang Xu   +5 more
openaire   +3 more sources

Improved Skills for Predicting Sub‐Seasonal to Seasonal Global Sea Surface Temperature

Advanced Science, EarlyView.
Addressing the critical Sub‐seasonal to Seasonal predictability gap, this study presents CAS‐Canglong, a novel deep learning framework. By leveraging 3D Swin‐Transformers to capture long‐range ocean interactions, the model demonstrates superior skill over traditional GCMs.
Longhao Wang, Xuanze Zhang, Ruby L. Leung, Francis H. S. Chiew, Amir AghaKouchak, Kairan Ying, Yongqiang Zhang   +6 more
wiley   +1 more source

Brain Tumor Detection using Swin Transformers

CoRR, 2023
The first MRI scan was done in the year 1978 by researchers at EML Laboratories. As per an estimate, approximately 251,329 people died due to primary cancerous brain and CNS (Central Nervous System) Tumors in the year 2020. It has been recommended by various medical professionals that brain tumor detection at an early stage would help in saving many ...
Prateek A. Meshram, Suraj Joshi, Devarshi Mahajan   +2 more
openaire   +3 more sources

When Biology Meets Medicine: A Perspective on Foundation Models

Advanced Intelligent Discovery, EarlyView.
Artificial intelligence, and foundation models in particular, are transforming life sciences and medicine. This perspective reviews biological and medical foundation models across scales, highlighting key challenges in data availability, model evaluation, and architectural design.
Kunying Niu, Yuxing Wang, Limin Zou, Jing Huang   +3 more
wiley   +1 more source

MV-Swin-T: Mammogram Classification with Multi-View Swin Transformer

2024 IEEE International Symposium on Biomedical Imaging (ISBI)
Traditional deep learning approaches for breast cancer classification has predominantly concentrated on single-view analysis. In clinical practice, however, radiologists concurrently examine all views within a mammography exam, leveraging the inherent correlations in these views to effectively detect tumors. Acknowledging the significance of multi-view
Sushmita Sarker, Prithul Sarker, George Bebis, Alireza Tavakkoli   +3 more
openaire   +5 more sources

Small Object Detection for Birds with Swin Transformer

2023 18th International Conference on Machine Vision and Applications (MVA), 2023
Object detection is the task of detecting objects in an image. In this task, the detection of small objects is particularly difficult. Other than the small size, it is also accompanied by difficulties due to blur, occlusion, and so on. Current small object detection methods are tailored to small and dense situations, such as pedestrians in a crowd or ...
Da Huo, Marc A. Kastner 0001, Tingwei Liu, Yasutomo Kawanishi, Takatsugu Hirayama, Takahiro Komamizu, Ichiro Ide   +6 more
openaire   +3 more sources

FEA-Swin: Foreground Enhancement Attention Swin Transformer Network for Accurate UAV-Based Dense Object Detection

, 2022
UAV-based object detection has recently attracted a lot of attention due to its diverse applications. Most of the existing convolution neural network based object detection models can perform well in common object detection cases.
Zhang, Chaofan, Chaofan Zhang, Wang, Qi, Dai, Pangda, Qi Wang, Pangda Dai, Xu, Wenyu, Wenyu Xu   +7 more
core   +1 more source