Results 51 to 60 of about 2,105 (150)
Deep Reinforcement Learning with Swin Transformers
Proceedings of the 2024 8th International Conference on Digital Signal ProcessingTransformers 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 +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 +5 more
openaire +3 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 +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 +2 more
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Advanced Deep Learning and Hybrid Architectures in Biomedical Data Analysis for Advances in Medicine
Medicine Advances, EarlyView.This review summarizes AI methods for biomedical imaging and clinical data analysis. Deep learning and multimodal models improve feature learning and diagnostic accuracy. Future progress requires explainable and generalizable AI for precision medicine.
Lifeng Li +4 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 +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 +6 more
openaire +3 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 +3 more
wiley +1 more source
DarSwin: Distortion Aware Radial Swin Transformer
2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023 18 pages, 12 ...
Akshaya Athwale +5 more
openaire +2 more sources
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 +5 more
wiley +1 more source