Emerging trends in fast MRI using deep-learning reconstruction on undersampled k-space data: a systematic review
Magnetic Resonance Imaging (MRI) is an essential medical imaging modality that provides
excellent soft-tissue contrast and high-resolution images of the human body, allowing us to …
excellent soft-tissue contrast and high-resolution images of the human body, allowing us to …
AGGN: Attention-based glioma grading network with multi-scale feature extraction and multi-modal information fusion
In this paper, a magnetic resonance imaging (MRI) oriented novel attention-based glioma
grading network (AGGN) is proposed. By applying the dual-domain attention mechanism …
grading network (AGGN) is proposed. By applying the dual-domain attention mechanism …
Classification of brain tumours in MR images using deep spatiospatial models
A brain tumour is a mass or cluster of abnormal cells in the brain, which has the possibility of
becoming life-threatening because of its ability to invade neighbouring tissues and also form …
becoming life-threatening because of its ability to invade neighbouring tissues and also form …
1% vs 100%: Parameter-efficient low rank adapter for dense predictions
Fine-tuning large-scale pre-trained vision models to downstream tasks is a standard
technique for achieving state-of-the-art performance on computer vision benchmarks …
technique for achieving state-of-the-art performance on computer vision benchmarks …
Hybrid Inception Architecture with Residual Connection: Fine-tuned Inception-ResNet Deep Learning Model for Lung Inflammation Diagnosis from Chest Radiographs
Diagnosing lung inflammation, particularly pneumonia, is of paramount importance for
effectively treating and managing the disease. Pneumonia is a common respiratory infection …
effectively treating and managing the disease. Pneumonia is a common respiratory infection …
[PDF][PDF] Fine-tuning transfer learning model in woven fabric pattern classification
It is important to figure out the patterns of woven fabrics before producing woven fabric with a
machine. Recognition of woven fabric pattern usually with the help of the human eye can …
machine. Recognition of woven fabric pattern usually with the help of the human eye can …
Improved Yolov8 and Sahi Model for the Collaborative Detection of Small Targets at the Micro Scale: A Case Study of Pest Detection in Tea
R Ye, Q Gao, Y Qian, J Sun, T Li - Agronomy, 2024 - mdpi.com
Pest target identification in agricultural production environments is challenging due to the
dense distribution, small size, and high density of pests. Additionally, changeable …
dense distribution, small size, and high density of pests. Additionally, changeable …
MetaQA: Enhancing human-centered data search using Generative Pre-trained Transformer (GPT) language model and artificial intelligence
Accessing and utilizing geospatial data from various sources is essential for developing
scientific research to address complex scientific and societal challenges that require …
scientific research to address complex scientific and societal challenges that require …
Parameter-efficient is not sufficient: Exploring parameter, memory, and time efficient adapter tuning for dense predictions
Pre-training & fine-tuning is a prevalent paradigm in computer vision (CV). Recently,
parameter-efficient transfer learning (PETL) methods have shown promising performance in …
parameter-efficient transfer learning (PETL) methods have shown promising performance in …
A method to estimate leaf area index from VIIRS surface reflectance using deep transfer learning
J Li, Z Xiao, R Sun, J Song - ISPRS Journal of Photogrammetry and …, 2023 - Elsevier
The leaf area index (LAI) retrieval methods based on traditional neural networks require a
large number of training samples constructed from remote sensing data or simulation data …
large number of training samples constructed from remote sensing data or simulation data …