Prediction of tumor origin in cancers of unknown primary origin with cytology-based deep learning

Prediction of tumor origin in cancers of unknown primary origin with cytology-based deep learning

Information Science, Artificial Intelligence, Medicine, Oncology, Life Sciences, Cell Biology,
Cancer   Deep Learning   Tumor origin   Cytology   Unknown primary  

Background Introduction Cancer of Unknown Primary (CUP) is a type of malignant disease that is confirmed to be metastatic through histopathology but whose primary site cannot be identified using conventional baseline diagnostic methods. CUP presents significant diagnostic and therapeutic challenges in clinical practice and is believed to account fo...

Medical History Predicts Phenome-Wide Disease Onset and Enables the Rapid Response to Emerging Health Threats

Information Science, Artificial Intelligence, Medicine, Environmental and Public Health,
medical history   disease prediction   rapid response   COVID-19   neural network   risk stratification  

Using Medical Records to Predict Common Disease Incidence and Support Rapid Response to Emerging Health Threats Research Background and Motivation The COVID-19 pandemic exposed systemic issues and a lack of data-driven guidance globally, significantly affecting the identification of high-risk populations and pandemic preparedness. Assessing individ...

Diffusion Model Optimization with Deep Learning

Diffusion Model Optimization with Deep Learning

Information Science, Artificial Intelligence, Medicine, Neurology, Medical Imaging, Life Sciences, Biophysics,
deep learning   diffusion model   neural network   microstructural imaging   self-supervised learning  

Dimond: A Study on Optimizing Diffusion Models through Deep Learning Academic Background In brain science and clinical applications, Diffusion Magnetic Resonance Imaging (dMRI) is an essential tool for non-invasively mapping the microstructure and neural connectivity of brain tissue. However, accurately estimating parameters of the diffusion signal...

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...

Sarcoma microenvironment cell states and ecosystems are associated with prognosis and predict response to immunotherapy

Sarcoma microenvironment cell states and ecosystems are associated with prognosis and predict response to immunotherapy

Information Science, Artificial Intelligence, Medicine, Rheumatology and Immunology, Orthopedics, Oncology, Life Sciences, Immunology,
sarcoma   microenvironment   cell states   ecosystems   immunotherapy  

This study utilized a machine learning framework to explore the underlying cell states and cellular ecosystems constituting soft tissue sarcomas, and associated them with patient prognosis and response to immunotherapy. Research Background: Soft tissue sarcomas are rare and highly heterogeneous malignancies of connective tissues, with limited syste...

Geometry-enhanced pretraining on interatomic potentials

Chemistry, Computational Chemistry, Information Science, Computer Science, Artificial Intelligence,
machine learning   molecular dynamics   graph neural networks   self-supervised learning   interatomic potentials  

Geometric Enhanced Pretraining for Interatomic Potentials Introduction Molecular dynamics (MD) simulations play an important role in fields such as physics, chemistry, biology, and materials science, providing insights into atomic-level processes. The accuracy and efficiency of MD simulations depend on the choice of interatomic potential functions ...