A5027855761- Radiomics and Machine Learning in Medical Imaging
- Advanced Radiotherapy Techniques
- Medical Imaging Techniques and Applications
- AI in cancer detection
- Ultrasound Imaging and Elastography
- Ultrasound and Hyperthermia Applications
- Advanced Neural Network Applications
- Advanced X-ray and CT Imaging
- Thyroid Cancer Diagnosis and Treatment
- Photoacoustic and Ultrasonic Imaging
- Medical Image Segmentation Techniques
- Brain Tumor Detection and Classification
- Gastric Cancer Management and Outcomes
- Advanced Image Processing Techniques
- RNA modifications and cancer
- Medical Imaging and Analysis
- Lung Cancer Diagnosis and Treatment
- Ferroptosis and cancer prognosis
- Breast Lesions and Carcinomas
- Colorectal Cancer Surgical Treatments
- Functional Brain Connectivity Studies
- Liver Disease Diagnosis and Treatment
- Breast Cancer Treatment Studies
- Generative Adversarial Networks and Image Synthesis
- MRI in cancer diagnosis
Karolinska Institutet
2023-2025
Zhejiang Shuren University
2022-2025
Nanjing Medical University
2021-2024
Changzhou No.2 People's Hospital
2021-2024
Second Affiliated Hospital of Harbin Medical University
2013-2024
Harbin Medical University
2013-2024
Beijing Children’s Hospital
2024
Capital Medical University
2024
Shaanxi Normal University
2024
Nanjing Drum Tower Hospital
2023
Abstract Memory is a crucial cognitive function that deteriorates with age. However, this ability normally assessed using tests instead of the architecture brain networks. Here, we use reservoir computing, recurrent neural network computing paradigm, to assess linear memory capacities neural-network reservoirs extracted from anatomical connectivity data in lifespan cohort 636 individuals. The computational capacity emerges as robust marker aging, being associated resting-state functional...
Gliomas are the most common primary brain tumors, and objective grading is of great importance for treatment. This paper presents an automatic computer-aided diagnosis gliomas that combines segmentation radiomics, which can improve diagnostic ability. The MRI data containing 220 high-grade 54 low-grade used to evaluate our system. A multiscale 3D convolutional neural network trained segment whole tumor regions. wide range radiomic features including first-order features, shape texture...
Small cell lung cancer (SCLC) is one of the most common types malignant tumors, characterized by rapid growth and early metastasis spread. Early accurate diagnosis SCLC vital for improved survival. Accurate segmentation helps doctors understand location size make better diagnostic decisions. However, manual cancers from large amounts medical images a time-consuming challenging task. In this paper, we propose hybrid network (referred to as HSN) based on convolutional neural (CNN)...
To develop high-quality synthetic CT (sCT) generation method from low-dose cone-beam (CBCT) images by using attention-guided generative adversarial networks (AGGAN) and apply these to dose calculations in radiotherapy. The CBCT/planning of 170 patients undergoing thoracic radiotherapy were used for training testing. CBCT scanned under a fast protocol with 50% less clinical projection frames compared standard chest M20 protocol. Training aligned paired was performed conditional (so-called...
Abstract Gliomas segmentation is a critical and challenging task in surgery treatment, it also the basis for subsequent evaluation of gliomas. Magnetic resonance imaging extensively employed diagnosing brain nervous system abnormalities. However, tumor remains task, because differentiating tumors from normal tissues difficult, boundaries are often ambiguous there high degree variability shape, location, extent patient. It therefore desired to devise effective image architectures. In past few...
Cone-beam computed tomography (CBCT) is widely used for daily image guidance in radiation therapy, enhancing the reproducibility of patient setup. However, its application adaptive radiotherapy (ART) limited by many imaging artifacts and inaccurate Hounsfield units (HUs). The correction CBCT necessary great value CBCT-based ART.To explore synthetic CT (sCT) generation from images thorax abdomen patients, which usually surfer serious duo to organ state changes. In this study, a streaking...
bone mineral density (BMD) is strongly associated with the risk of osteoporosis and fractures. Furthermore, dietary tea consumption also has a great impact on variation in BMD. The pathway mechanisms from to BMD are not well known. Therefore, we applied two-sample Mendelian randomization (MR) approach an attempt explore causality between And then examine whether effects intake specific across different age groups. investigated relationship using analysis, utilizing 31 single nucleotide...
Abstract Background The epithelial-mesenchymal transition (EMT) plays a pivotal role in various physiological processes, such as embryonic development, tissue morphogenesis, and wound healing. EMT also an important cancer invasion, metastasis, chemoresistance. Additionally, is partially responsible for chemoresistance colorectal (CRC). aim of this research to develop EMT-based prognostic signature CRC. Methods RNA-seq microarray data, together with clinical information, were downloaded from...
To determine the diagnostic performance and inter-reader agreement of contrast-enhanced ultrasound liver imaging reporting data system (CEUS-LI-RADS) for diagnosing hepatocellular carcinoma (HCC) in high-risk patients. In this prospective study, CEUS-LI-RADS categories (LR-5 predicting HCC) were assigned by six blinded readers compared to definitive HCC diagnosis patients with cirrhosis per 2017 China Liver Cancer Guidelines (CLCG). CEUS features recorded 96 histology-proven lesions. The...
Objective.A multi-discriminator-based cycle generative adversarial network (MD-CycleGAN) model is proposed to synthesize higher-quality pseudo-CT from MRI images.Approach.MRI and CT images obtained at the simulation stage with cervical cancer were selected train model. The generator adopted DenseNet as main architecture. local global discriminators based on a convolutional neural jointly discriminated authenticity of input image data. In testing phase, was verified by fourfold...
To establish a predictive model incorporating clinical features and contrast enhanced ultrasound liver imaging reporting data system (CEUS LI-RADS) for estimation of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients.
In modern radiotherapy, error reduction in the patients' daily setup is important for achieving accuracy. our study, we proposed a new approach development of an assist system radiotherapy position by using augmented reality (AR). We aimed to improve accuracy patients undergoing and evaluate who were diagnosed with head neck cancer, that chest abdomen cancer. acquired patient's simulation CT data three-dimensional (3D) reconstruction external surface organs. The AR tracking software detected...
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...
Deep neural networks (DNNs) thrive in recent years, wherein batch normalization (BN) plays an indispensable role. However, it has been observed that BN is costly due to the huge reduction and elementwise operations are hard be executed parallel, which heavily reduces training speed. To address this issue, article, we propose a methodology alleviate BN's cost by using only few sampled or generated data for mean variance estimation at each iteration. The key challenge reach goal how achieve...