|
序号
|
成果名称
|
期刊名称
|
影响因子
|
|
1
|
A semantic feature-enhanced radiomics model based on spinal MRI for predicting early relapse in newly diagnosed multiple myeloma: A multi-center retrospective study
|
European Journal of Radiology
|
3.3
|
|
2
|
MRMS-CNNFormer: A Novel Framework for Predicting the Biochemical Recurrence of Prostate Cancer on Multi-Sequence MRI
|
Bioengineering
|
3.7
|
|
3
|
Multimodal deep learning model for predicting microsatellite instability in colorectal cancer by contrast-enhanced computed tomography and histopathology
|
European Journal of Radiology
|
3.3
|
|
4
|
Development of a multi-task deep learning system for classification of nine common knee abnormalities on MRI: a large-scale, multicentre, stepwise validation study
|
eClinicalMedicine
|
10
|
|
5
|
Enhanced diagnosis of axial spondyloarthritis using machine learning with sacroiliac joint MRI: a multicenter study
|
Insights Imaging
|
4.5
|
|
6
|
Collagen fibers quantification for liver fibrosis assessment using linear dichroism photoacoustic microscopy
|
Photoacoustics
|
6.8
|
|
7
|
鉴别原发性骨肿瘤骨样和软骨样基质矿化：基于CT和临床特征的深度学习融合模型的多中心回顾性研究
|
南方医科大学学报
|
1.9
|
|
8
|
Quantification of Vascular Remodeling and Sinusoidal Capillarization to Assess Liver Fibrosis with Photoacoustic Imaging
|
Radiology
|
12.1
|
|
9
|
A deep learning model to enhance the classification of primary bone tumors based on incomplete multimodal images in X-ray, CT, and MRI
|
Cancer Imaging
|
3.5
|
|
10
|
Artificial intelligence improves the diagnosis of human leukocyte antigen (HLA)-B27-negative axial spondyloarthritis based on multi-sequence magnetic resonance imaging and clinical features
|
Quant Imaging Med Surg
|
2.9
|
|
11
|
BreasTDLUSeg: A coarse-to-fine framework for segmentation of breast terminal duct lobular units on histopathological whole-slide images
|
Comput Med Imaging Graph
|
5.4
|
|
12
|
基于注意力机制和MRI的深度学习模型预测中轴型脊柱关节炎骶髂关节新骨形成进展
|
磁共振成像
|
1.7
|
|
13
|
Learning co-plane attention across MRI sequences for diagnosing twelve types of knee abnormalities
|
Nat Commun
|
15.2
|
|
14
|
A radiograph-based deep learning model improves radiologists, performance for classification of histological types of primary bone tumors: a multicenter study
|
European Journal of Radiology
|
3.4
|
|
15
|
Automatic segmentation of fat metaplasia on sacroiliac joint MRI using deep learning
|
Insights into Imaging
|
4.1
|
|
16
|
Mn3O4 Nanoshell Coated Metal-Organic Frameworks with Microenvironment-Driven O2 Production and GSH Exhaustion Ability for Enhanced Chemodynamic and Photodynamic Cancer Therapies.
|
Adv. Healthc. Mater
|
10.0
|
|
17
|
Automatic detection, segmentation, and classification of primary bone tumors and bone infections using an ensemble multi-task deep learning framework on multi-parametric MRIs: A multi-center study.
|
Eur Radio
|
4.7
|
|
18
|
Improved Performance of Compartments in Detecting the Activity of Axial Spondyloarthritis Based on IVIM DWI with Optimized Threshold b Value
|
BioMed Res. Int
|
2.6
|
|
19
|
Intravoxel incoherent motion diffusion-weighted imaging as a quantitative tool for evaluating disease activity in patients with axial spondyloarthritis
|
Clin Radiol
|
2.2
|
|
20
|
Intravoxel Incoherent Motion Imaging on Sacroiliitis in Patients With Axial Spondyloarthritis: Correlation With Perfusion Characteristics Based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging
|
Front. Med
|
5.4
|
|
21
|
Prediction of Treatment Response According to ASAS-EULAR Management Recommendations in 1 Year for Hip Involvement in Axial Spondyloarthritis Based on MRI and Clinical Indicators
|
Front. endocrinol
|
3.9
|
|
22
|
Detection of Active Sacroiliitis with Ankylosing Spondylitis through Intravoxel Incoherent Motion Diffusion-Weighted MR Imaging
|
Eur Radiol
|
3.640
|
|
23
|
MRI-based radiomics signature for pretreatment prediction of pathological response to neoadjuvant chemotherapy in osteosarcoma: a multicenter study
|
Eur Radiol
|
4.7
|
|
24
|
Pretreatment prediction of relapse risk in patients with osteosarcoma using radiomics nomogram based on CT: a retrospective multicenter study
|
BioMed Res. Int
|
2.6
|