Identification and Validation of a Prognostic Model Based on Three TLS-Related Genes in Oral Squamous Cell Carcinoma

Medicine, Oral Sciences, Medical Imaging, Oncology, Life Sciences, Biochemistry, Immunology, Genetics,
oral squamous cell carcinoma   tertiary lymphoid structure   prognostic model   immune infiltration   convolutional neural network  

Research Report: Validation and Analysis of a Prognostic Model for Oral Squamous Cell Carcinoma Constructed with TLS-Related Genes Background and Research Motivation Oral Squamous Cell Carcinoma (OSCC) is the most common subtype of Head and Neck Squamous Cell Carcinoma (HNSCC), exhibiting a high tendency for lymph node metastasis, especially to the...

Auto-Segmentation of Neck Nodal Metastases Using Self-Distilled Masked Image Transformer on Longitudinal MR Images

Auto-Segmentation of Neck Nodal Metastases Using Self-Distilled Masked Image Transformer on Longitudinal MR Images

Information Science, Artificial Intelligence, Medicine, Medical Imaging, Oncology,
deep learning   convolutional neural network   neck nodal metastases   auto-segmentation   self-supervised learning   vision transformers  

Potential of Self-Distilling Masked Image Transformer in Longitudinal MRI - Automatic Segmentation of Cervical Lymph Node Metastases Report Introduction In tumor radiotherapy, automatic segmentation technology promises to improve speed and reduce inter-reader variability caused by manual segmentation. In radiotherapy clinical practice, accurate and...

Noninvasive Grading of Glioma by Knowledge Distillation Based Lightweight Convolutional Neural Network

Information Science, Artificial Intelligence, Medicine, Neurosurgery, Oncology,
knowledge distillation   glioma grading   convolutional neural network   lightweight model   image classification  

Review of Non-Invasive Glioma Grading Research: Lightweight Convolutional Neural Networks Based on Knowledge Distillation Background Gliomas are the main tumors of the central nervous system, and early detection is crucial. The World Health Organization (WHO) classifies gliomas from grade I to IV, with grades I and II being low-grade gliomas (LGG) ...

An Attention-Guided CNN Framework for Segmentation and Grading of Glioma Using 3D MRI Scans

Information Science, Computer Science, Artificial Intelligence, Medicine, Medical Imaging, Oncology,
glioma   convolutional neural network   segmentation   magnetic resonance imaging   multi-task learning   attention mechanism  

Study of Attention-Guided CNN Framework for 3D MRI Glioma Segmentation and Grading Gliomas are the most deadly form of brain tumors in humans. Timely diagnosis of these tumors is a crucial step for effective tumor treatment. Magnetic Resonance Imaging (MRI) typically provides a non-invasive examination of brain lesions. However, manual inspection o...

A Novel CNN-Based Image Segmentation Pipeline for Individualized Feline Spinal Cord Stimulation Modeling

Information Science, Artificial Intelligence, Medicine, Neurology, Medical Imaging, Life Sciences, Bioengineering,
spinal cord stimulation   machine learning   automatic segmentation   axonal recruitment   convolutional neural network   artificial intelligence  

Automated Spinal Cord Segmentation Pipeline Based on Convolutional Neural Network (CNN) for Individualized Cat Spinal Cord Stimulation Modeling Background and Research Motivation Spinal cord stimulation (SCS) is a widely used treatment method for chronic pain management. In recent years, it has also been used to modulate neural activity, aiming to ...

Self-Supervised Deep Learning-Based Denoising for Diffusion Tensor MRI

Self-Supervised Deep Learning-Based Denoising for Diffusion Tensor MRI

Medicine, Medical Imaging, Life Sciences, Bioengineering,
self-supervised learning   deep learning   convolutional neural network   diffusion tensor imaging   denoising  

Background Introduction Diffusion Tensor Magnetic Resonance Imaging (DTI) is a widely used neuroimaging technique for imaging the microstructure of brain tissues and white matter tracts. However, noise in Diffusion-Weighted Images (DWI) can reduce the accuracy of microstructural parameters derived from DTI data and also necessitate longer acquisiti...

DeepDTI: High-Fidelity Six-Direction Diffusion Tensor Imaging Using Deep Learning

DeepDTI: High-Fidelity Six-Direction Diffusion Tensor Imaging Using Deep Learning

Information Science, Computer Science, Artificial Intelligence, Medicine, Neurology, Medical Imaging, Life Sciences, Biophysics,
diffusion tensor imaging   diffusion tractography   tract-specific analysis   deep learning   residual learning   convolutional neural network   data redundancy  

DeepDTI: High-Fidelity Six-Direction Diffusion Tensor Imaging Using Deep Learning Research Background and Motivation Diffusion Tensor Imaging (DTI) boasts unparalleled advantages in mapping the microstructure and structural connectivity of live human brain tissue. However, traditional DTI techniques require extensive angular sampling, leading to pr...