Brain tumor imaging without gadolinium-based contrast agents: feasible or fantasy?
Gadolinium-based contrast agents (GBCAs) form the cornerstone of current primary brain
tumor MRI protocols at all stages of the patient journey. Though an imperfect measure of …
tumor MRI protocols at all stages of the patient journey. Though an imperfect measure of …
A fully automated multimodal MRI-based multi-task learning for glioma segmentation and IDH genoty**
The accurate prediction of isocitrate dehydrogenase (IDH) mutation and glioma
segmentation are important tasks for computer-aided diagnosis using preoperative …
segmentation are important tasks for computer-aided diagnosis using preoperative …
Multimodal disentangled variational autoencoder with game theoretic interpretability for glioma grading
Effective fusion of multimodal magnetic resonance imaging (MRI) is of great significance to
boost the accuracy of glioma grading thanks to the complementary information provided by …
boost the accuracy of glioma grading thanks to the complementary information provided by …
MLDRL: Multi-loss disentangled representation learning for predicting esophageal cancer response to neoadjuvant chemoradiotherapy using longitudinal CT images
Accurate prediction of pathological complete response (pCR) after neoadjuvant
chemoradiotherapy (nCRT) is essential for clinical precision treatment. However, the …
chemoradiotherapy (nCRT) is essential for clinical precision treatment. However, the …
Mmgk: Multimodality multiview graph representations and knowledge embedding for mild cognitive impairment diagnosis
The diagnosis of mild cognitive impairment (MCI), which is an early stage of Alzheimer's
disease (AD), has great clinical significance. Medical imaging and gene sequencing …
disease (AD), has great clinical significance. Medical imaging and gene sequencing …
ResMT: A hybrid CNN-transformer framework for glioma grading with 3D MRI
Accurate grading of gliomas is crucial for treatment strategies and prognosis. While
convolutional neural networks (CNNs) have proven effective in classifying medical images …
convolutional neural networks (CNNs) have proven effective in classifying medical images …
Arsc-net: Adventitious respiratory sound classification network using parallel paths with channel-spatial attention
Automatic identification of adventitious respiratory sound has still been a challenging
problem in recent years. To address this challenge, we propose an adventitious respiratory …
problem in recent years. To address this challenge, we propose an adventitious respiratory …
Integrated diagnosis of glioma based on magnetic resonance images with incomplete ground truth labels
S Cao, Z Hu, X **e, Y Wang, J Yu, B Yang, Z Shi… - Computers in Biology …, 2024 - Elsevier
Background Since the 2016 WHO guidelines, glioma diagnosis has entered an era of
integrated diagnosis, combining tissue pathology and molecular pathology. The WHO has …
integrated diagnosis, combining tissue pathology and molecular pathology. The WHO has …
Hippocampal segmentation in brain MRI images using machine learning methods: A survey
The hippocampus is closely related to many brain diseases, such as Alzheimer's disease.
Accurate measurement of the hippocampus is helpful for clinicians in identifying lesions and …
Accurate measurement of the hippocampus is helpful for clinicians in identifying lesions and …
Graph-based fusion of imaging, genetic and clinical data for degenerative disease diagnosis
Graph learning methods have achieved noteworthy performance in disease diagnosis due
to their ability to represent unstructured information such as inter-subject relationships. While …
to their ability to represent unstructured information such as inter-subject relationships. While …