A5048235757- Radiomics and Machine Learning in Medical Imaging
- Medical Image Segmentation Techniques
- Prostate Cancer Diagnosis and Treatment
- Lymphoma Diagnosis and Treatment
- Advanced Neural Network Applications
- Cerebrovascular and Carotid Artery Diseases
- AI in cancer detection
- Cardiovascular Health and Disease Prevention
- Generative Adversarial Networks and Image Synthesis
- Medical Imaging Techniques and Applications
- MRI in cancer diagnosis
- Lung Cancer Diagnosis and Treatment
- Medical Imaging and Analysis
- Retinal Imaging and Analysis
- Sarcoma Diagnosis and Treatment
Changzhou Institute of Technology
2022-2025
Changzhou No.2 People's Hospital
2022-2025
Nanjing Medical University
2022-2025
Nanjing University of Science and Technology
2016-2020
Midea Group (China)
2020
University of North Carolina at Chapel Hill
2014-2016
Extracting the high-level feature representation by using deep neural networks for detection of prostate cancer, and then based on constructing hierarchical classification to refine results.High-level is first learned a learning network, where multiparametric MR images are used as input data. Then, features, method developed, multiple random forest classifiers iteratively constructed results cancer.The experiments were carried 21 real patient subjects, proposed achieves an averaged...
This study aimed to construct a radiomics-based imaging biomarker for the non-invasive identification of transformed follicular lymphoma (t-FL) using PET/CT images. A total 784 (FL), diffuse large B-cell lymphoma, and t-FL patients from 5 independent medical centers were included. The unsupervised EMFusion method was applied fuse PET CT Deep-based radiomic features extracted fusion images deep learning model (ResNet18). These features, along with handcrafted radiomics, utilized signature...
Diffuse large B-cell lymphoma (DLBCL), a cancer of B cells, has been one the most challenging and complicated diseases because its considerable variation in clinical behavior, response to therapy, prognosis. Radiomic features from medical images, such as PET have become valuable for disease classification or prognosis prediction using learning-based methods. In this paper, new flexible ensemble deep learning model is proposed DLBCL 18F-FDG images. This study proposes multi-R-signature...