Results 41 to 50 of about 7,252 (198)

HEAL-SWIN: A Vision Transformer on the Sphere

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Accepted as poster to CVPR 2024. Main body: 10 pages, 7 figures.
Oscar Carlsson, Jan E. Gerken, Hampus Linander, Heiner Spieß, Fredrik Ohlsson, Christoffer Petersson, Daniel Persson   +6 more
openaire   +2 more sources

MoR–Swin: Efficient Vision Transformer Using Mixture of Recursions

Journal of Imaging
Vision Transformers, especially Swin Transformer, have become default backbones for various vision tasks but suffer from high memory consumption and training costs. This letter proposes MoR–Swin, a novel architecture that integrates Mixture of Recursions
Yongbao Ai, Tianxiang Gao, Zhipeng Lin, Longqi Yang, Qingyu Chang   +4 more
doaj   +1 more source

Usability of a deep learning platform for detecting radiographic bone loss and furcation involvement

Journal of Periodontology, EarlyView.
Abstract Background Assessing radiographic bone condition is important for periodontal diagnosis. The accuracy of radiographic interpretation depends highly on a clinician's experience and knowledge. This study aimed to develop a deep learning‐based online platform that aids clinicians in diagnosing periodontitis based on periapical radiographs and to ...
Chun‐Teh Lee, Tanjida Kabir, Alaa Alsaffar, Jiman Nelson, Sally Sheng, Hsiu‐Wan Meng, Mohammad Qali, Muhammad F. Walji, Xiaoqian Jiang, Shayan Shams   +9 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   +2 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   +2 more sources

Multiomics Insights Into AL Amyloidosis

Medicine Bulletin, EarlyView.
ABSTRACT Light chain amyloidosis is a systemic or localized protein conformational disorder triggered by misfolded immunoglobulin light chains, leading to amyloid fibril deposition. The disease is characterized by multiorgan involvement and delayed diagnosis, contributing to poor prognosis and high mortality rates.
Zixuan Zhang, Ziru Huang, Haoxiang Tang, Xianrun Pan, Rong Wang, Dingding Zhang, Jian Huang   +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   +2 more sources

Deep‐DSP2: Cross‐Domain Deep Learning Direct MR Signal Prediction for RF Shielding‐Free MRI

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose To develop a deep learning approach to electromagnetic interference (EMI) elimination in the presence of dynamically varying electromagnetic coupling relationships (i.e., spectral domain transfer functions) between MRI receive and EMI sensing coils for RF shielding‐free ultra‐low‐field (ULF) MRI.
Jiahao Hu, Yujiao Zhao, Alex T. L. Leong, Ed X. Wu   +3 more
wiley   +1 more source

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   +2 more sources

R3Net: Recursive Residual Refinement Network Architecture for Decoder‐Free Medical Image Segmentation

Precision Radiation Oncology, EarlyView.
We propose R3Net, a decoder‐free medical image segmentation framework that recursively refines multiscale representations within the encoder using residual pathways. R3Net achieves competitive accuracy with reduced model complexity and improved computational efficiency across multiple medical imaging modalities.
Jing Huang, Yongkang Zhao, Yuhan Li, Zhitao Dai, Cheng Chen, Qiying Lai   +5 more
wiley   +1 more source