Results 71 to 80 of about 192,101 (191)
Fine-Grained Ship Classification by Combining CNN and Swin Transformer
, 2022 The mainstream algorithms used for ship classification and detection can be improved based on convolutional neural networks (CNNs). By analyzing the characteristics of ship images, we found that the difficulty in ship image classification lies in ...
Yalun Zhang +3 more
core +1 more source
Comparing deep learning models for butterfly and moth (Lepidoptera) species identification
Insect Conservation and Diversity, EarlyView.Deep learning models open new possibilities for the processing of image‐based species records. We used high‐performance computing to compare 40 models for butterfly and moth species identification based on a citizen science dataset with over 500,000 images of 162 species.
Friederike Barkmann +2 more
wiley +1 more source
International Endodontic Journal, EarlyView.ABSTRACT Background Panoramic radiographs are used routinely to screen dental conditions and treatment patterns. Recently, numerous studies have suggested that deep learning (DL) models can be utilized for analysing panoramic radiographs. Objective This review aimed to evaluate the accuracy of DL models in detecting periapical radiolucent lesions (PRLs)
Ibrahim Ali Ahmad +3 more
wiley +1 more source
Few-Shot Image Classification Algorithm of Graph Neural Network Based on Swin Transformer
In fewshot image classification tasks, capturing remote semantic information in feature extraction modules based on convolutional neural network and single measure of edgefeature similarity are challenging.
Zhang, W, Ren, J, Wang, K
core +1 more source
Residual swin transformer for classifying the types of cotton pests in complex background
Frontiers in Plant ScienceBackgroundCotton pests have a major impact on cotton quality and yield during cotton production and cultivation. With the rapid development of agricultural intelligence, the accurate classification of cotton pests is a key factor in realizing the precise
Ting Zhang +9 more
doaj +1 more source
Journal of Microscopy, EarlyView.Abstract X‐ray phase contrast imaging (XPCI), when implemented in micro‐computed tomography (micro‐CT) mode, offers high‐contrast 3D imaging of weakly‐attenuating material samples. In the so‐called single‐mask edge illumination approach, a mask with periodically spaced transmitting apertures is used to split the x‐ray beam into narrow beamlets; when ...
Khushal Shah +8 more
wiley +1 more source
Remote SensingTransformer-based deep learning techniques have recently shown outstanding potential in remote sensing scene classification (RSSC), benefiting from their ability to capture global semantic relationships and contextual dependencies.
Xiaozhang Zhu +2 more
doaj +1 more source
Sample‐Efficient Transfer Learning for Histopathological Detection of HNSCC
Journal of Oral Pathology &Medicine, EarlyView.ABSTRACT Background Head and neck squamous cell carcinoma (HNSCC) is a common malignancy with increasing incidence. Histopathological assessment remains the diagnostic gold standard, but is labor‐intensive and affected by a growing shortage of specialized pathologists.
Till Nicke +10 more
wiley +1 more source
Artificial Intelligence in Orthodontics: Part 1—Basic Concepts
Orthodontics &Craniofacial Research, EarlyView.ABSTRACT Artificial intelligence (AI) is increasingly being integrated into orthodontic research and clinical practice. This review paper consists of three parts and aims to establish a foundational understanding of AI concepts for readers from non‐technical backgrounds while familiarising them with its applications and challenges within the field of ...
Fatemeh Sohrabniya +6 more
wiley +1 more source
Comparison of AR between the Swin-cryoEM model and Swin Transformer model.
Comparison of AR between the Swin-cryoEM model and Swin Transformer model.
JinLing Wang (18334331) +6 more
core +1 more source