Hematology

Lymphoma

Latest AI and machine learning research in lymphoma for healthcare professionals.

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Distilling knowledge from graph neural networks trained on cell graphs to non-neural student models.

The development and refinement of artificial intelligence (AI) and machine learning algorithms have ...

Non-coding genetic elements of lung cancer identified using whole genome sequencing in 13,722 Chinese.

A substantial portion of lung cancer-associated genetic elements in East Asian populations remains u...

A Deep Learning Model to Detect Acute MCA Occlusion on High Resolution Non-Contrast Head CT.

BACKGROUND AND PURPOSE: To assess the feasibility and accuracy of a deep learning (DL) model to iden...

An Anisotropic Cross-View Texture Transfer with Multi-Reference Non-Local Attention for CT Slice Interpolation.

Computed tomography (CT) is one of the most widely used non-invasive imaging modalities for medical ...

MRI-based radiomics for preoperative T-staging of rectal cancer: a retrospective analysis.

PUROPOSE: Preoperative T-staging in rectal cancer is essential for treatment planning, yet conventio...

Advanced non-destructive detection of peanut adulteration in ground roasted hazelnut using FT-NIR spectroscopy and machine learning.

Hazelnut adulteration with ground peanut is a severe health and economic problem. In this study, Fou...

LKDA-Net: Hierarchical transformer with large Kernel depthwise convolution attention for 3D medical image segmentation.

Since Transformers have demonstrated excellent performance in the segmentation of two-dimensional me...

NSPLformer: exploration of non-stationary progressively learning model for time series prediction.

Although Transformers perform well in time series prediction, they struggle when dealing with real-w...

Memory-enhanced and multi-domain learning-based deep unrolling network for medical image reconstruction.

Reconstructing high-quality images from corrupted measurements remains a fundamental challenge in me...

Development and evaluation of large-language models (LLMs) for oncology: A scoping review.

Large language models (LLMs), a significant development in artificial intelligence (AI), are continu...

Non-invasive acoustic classification of adult asthma using an XGBoost model with vocal biomarkers.

Traditional diagnostic methods for asthma, a widespread chronic respiratory illness, are often limit...

Adaptive resetting for informed search strategies and the design of non-equilibrium steady-states.

Stochastic resetting, the procedure of stopping and re-initializing random processes, has recently e...

Single cell density prediction based on optically induced electrokinetics (OEK) and machine learning.

Single cell density is a key indicator for judging cell physiological state, crucial for studying ce...

Limits on the computational expressivity of non-equilibrium biophysical processes.

Many biological decision-making tasks require classifying high-dimensional chemical states. The biop...

Clinical correlates of errors in machine-learning diagnostic model of autism spectrum disorder: Impact of sample cohorts.

Machine-learning models can assist in diagnosing autism but have biases. We examines the correlates ...

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