Results 41 to 50 of about 25,150 (262)

Improving diagnosis and prognosis of lung cancer using vision transformers: a scoping review

BMC Medical Imaging, 2023
Background Vision transformer-based methods are advancing the field of medical artificial intelligence and cancer imaging, including lung cancer applications.
Hazrat Ali, Farida Mohsen, Zubair Shah
doaj   +1 more source

Transformer Models for Vision

, 2023
The recent developments of deep learning cover a wide variety of tasks such as image classification, text translation, playing go, and folding proteins. All these successful methods depend on a gradient-based learning algorithm to train a model on massive amounts of data using significant computation power.
openaire   +1 more source

V-LTCS: Backbone exploration for Multimodal Misogynous Meme detection

Natural Language Processing Journal
Memes have become a fundamental part of online communication and humour, reflecting and shaping the culture of today’s digital age. The amplified Meme culture is inadvertently endorsing and propagating casual Misogyny. This study proposes V-LTCS (Vision-
Sneha Chinivar, Roopa M.S., Arunalatha J.S., Venugopal K.R.   +3 more
doaj   +1 more source

RT-ViT: Real-Time Monocular Depth Estimation Using Lightweight Vision Transformers

Sensors, 2022
The latest research in computer vision highlighted the effectiveness of the vision transformers (ViT) in performing several computer vision tasks; they can efficiently understand and process the image globally unlike the convolution which processes the ...
Hatem Ibrahem, Ahmed Salem, Hyun-Soo Kang   +2 more
doaj   +1 more source

GaitTriViT and GaitVViT: Transformer-based methods emphasizing spatial or temporal aspects in gait recognition [PDF]

PeerJ Computer Science
In image recognition tasks, subjects with long distances and low resolution remain a challenge, whereas gait recognition, identifying subjects by walking patterns, is considered one of the most promising biometric technologies due to its stability and ...
Hongyun Sheng
doaj   +2 more sources

Transforming glaucoma diagnosis: transformers at the forefront

Frontiers in Artificial Intelligence
Although the Vision Transformer architecture has become widely accepted as the standard for image classification tasks, using it for object detection in computer vision poses significant challenges.
Farheen Chincholi, Harald Koestler
doaj   +1 more source

Quantum Vision Transformers

Quantum
In this work, quantum transformers are designed and analysed in detail by extending the state-of-the-art classical transformer neural network architectures known to be very performant in natural language processing and image analysis. Building upon the previous work, which uses parametrised quantum circuits for data loading and orthogonal neural layers,
El Amine Cherrat, Iordanis Kerenidis, Natansh Mathur, Jonas Landman, Martin Strahm, Yun Yvonna Li   +5 more
openaire   +3 more sources

Comparative Effectiveness and Safety of Inebilizumab Versus Rituximab in AQP4‐IgG‐Positive NMOSD

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Rituximab (anti‐CD20, RTX) and inebilizumab (anti‐CD19, INE) represent B‐cell‐depleting therapies used for aquaporin‐4 antibody‐positive (AQP4‐IgG+) neuromyelitis optica spectrum disorder (NMOSD); however, direct comparative evidence remains limited.
Jie Lin, Binbin Xue, Dewei Xie, Jia Li, Juyuan Pan, Lanbing Zhu, Qiaowen Tong, Jing Lin, Ruofan Zhu, Xu Zhang, Junhui Xia, Xiang Li   +11 more
wiley   +1 more source

Leveraging Transfer Learning for Accurate CRP Level Prediction in Diabetic Patients

Zanco Journal of Pure and Applied Sciences
This study addresses the challenge of predicting C-reactive protein (CRP) levels in patients with type 2 diabetes mellitus by integrating tabular-to-image transformation techniques with transfer learning architectures.
Alaa J. Albaqal, Mardin A. Anwar
doaj   +1 more source

MOGAD Is the Most Common Cause of Isolated Optic Neuritis in Children

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objectives The study aimed to characterize the clinical features, etiologies, and outcomes of isolated, first‐time pediatric ON in the post‐MOG‐IgG era. Methods This was a single‐center retrospective cohort study at Texas Children's Hospital of patients diagnosed with first‐time ON between 2018–2024, with follow‐up data collected through 2025.
Chaitanya Aduru, Akansha Chandrasekar, Kyla Blasingame, Jonathan Rosen, Jesse M. Levine, Karla Salazar, Madhuri Chilakapati, Rod Foroozan, Victoria Hardwick, Jonathan M. Yarimi, Nikita Shukla, Timothy E. Lotze, Kristen S. Fisher, Alexander J. Sandweiss   +13 more
wiley   +1 more source