Results 101 to 110 of about 15,271 (229)

Spatio-Temporal Hybrid Fusion of CAE and SWIn Transformers for Lung Cancer Malignancy Prediction

, 2022
The paper proposes a novel hybrid discovery Radiomics framework that simultaneously integrates temporal and spatial features extracted from non-thin chest Computed Tomography (CT) slices to predict Lung Adenocarcinoma (LUAC) malignancy with minimum ...
Afshar, Parnian, Heidarian, Shahin, Khademi, Sadaf, Mohammadi, Arash, Naderkhani, Farnoosh, Oikonomou, Anastasia, Plataniotis, Konstantinos   +6 more
core  

Leveraging Atmospheric Information Across Scales for Local Temperature Forecasting Using a Novel Multi‐Scale Perception Network

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 2, April 2026.
Abstract Regional weather forecasting systems typically depend on boundary conditions from global models to represent large‐scale atmospheric processes, while such coupling increases complexity and hinders end‐to‐end optimization for specific target locations. Here, we propose the Multi‐scale Perception Network (MPN), a unified deep learning model that
Han Wang, Yilin Chen, Jiachuan Yang
wiley   +1 more source

A New Subject-Sensitive Hashing Algorithm Based on Multi-PatchDrop and Swin-Unet for the Integrity Authentication of HRRS Image

ISPRS International Journal of Geo-Information
Transformer-based subject-sensitive hashing algorithms exhibit good integrity authentication performance and have the potential to ensure the authenticity and convenience of high-resolution remote sensing (HRRS) images.
Kaimeng Ding, Yingying Wang, Chishe Wang, Ji Ma   +3 more
doaj   +1 more source

Zeeman: A Deep Learning Framework for Regional Atmospheric Chemistry Forecasting

Geophysical Research Letters, Volume 53, Issue 6, 28 March 2026.
Abstract Atmospheric chemistry encapsulates the emission of various pollutants, the complex chemistry reactions, and the meteorology dominant transport, which form a dynamic system that governs air quality. While deep learning (DL) models have shown promise in capturing intricate patterns for forecasting individual atmospheric components—such as PM2.5 $
Mijie Pang, Jianbing Jin, Arjo Segers, Hai Xiang Lin, Guoqiang Wang, Hong Liao, Wei Han   +6 more
wiley   +1 more source

Vision‐Language Models for Automated Chest X‐ray Interpretation: Leveraging ViT and GPT‐2

Engineering Reports
Radiology plays a pivotal role in modern medicine due to its non‐invasive diagnostic capabilities. However, the manual generation of unstructured medical reports is time‐consuming and prone to errors.
Md. Rakibul Islam, Md. Zahid Hossain, Mustofa Ahmed, Most. Sharmin Sultana Samu   +3 more
doaj   +1 more source

A Recognition and Classification Method for Underground Acoustic Emission Signals Based on Improved CELMD and Swin Transformer Neural Networks

Applied Sciences
To address the challenges in processing and identifying mine acoustic emission signals, as well as the inefficiency and inaccuracy issues prevalent in existing methods, an enhanced CELMD approach is adopted for preprocessing the acoustic emission signals.
Xuebin Xie, Yunpeng Yang
doaj   +1 more source

A novel framework for rosacea detection using Swin Transformers and explainable artificial intelligence

Alexandria Engineering Journal
Accurate and efficient diagnosis of skin rosacea is crucial in dermatological healthcare, yet remains challenging due to the need for precise classification and interpretability.
Anjali T, S. Abhishek, Remya S
doaj   +1 more source

Transfer Learning With U-Swin Transformer for Adaptive Ground Roll Attenuation in Seismic Records From Sabkha Environments

IEEE Access
Ground roll is a dominant coherent noise in land seismic data, characterized by low frequency, low velocity, and high amplitude. often overlaps with reflection arrivals, thereby reducing the reliability of seismic imaging and interpretation.
Ahmed Eleslambouly, Guibin Zhao, Fateh Bouchaala, Mohammed Y. Ali, Jun Matsushima   +4 more
doaj   +1 more source

Detection Method of Concave Defect on Specular Surfaces Based on Swin Transformer [PDF]

Shallow concave defects on mirrored surfaces are difficult to detect automatically. This paper proposes a defect detection method using a deep neural network (DNN) that learns the presence or absence of distortion in the image of a stripe pattern ...
Tanaka, Kazumoto
core   +2 more sources

A self attention based deep learning framework for accurate and efficient dental disease detection in OPG radiographs

Scientific Reports
Oral diseases are increasing now-a-days and there is a high demand for the automatic diagnostic system that helps the clinician to detect these oral diseases with more accuracy and reduced human error.
Ramasubramanian Bhoopalan, S. Mirdula, Priyadharshini Kannusamy, D. Gayathri, D. Manikandan, Krishnaraj Ramaswamy   +5 more
doaj   +1 more source