Hematology

Lymphoma

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

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MRI denoising with a non-blind deep complex-valued convolutional neural network.

MR images with high signal-to-noise ratio (SNR) provide more diagnostic information. Various methods...

Integrating radiomic and 3D autoencoder-based features for Non-Small Cell Lung Cancer survival analysis.

BACKGROUND AND OBJECTIVES: The aim of this study is to develop a radiomic and deep learning-based si...

A deep learning model for prediction of autism status using whole-exome sequencing data.

Autism is a developmental disability. Research demonstrated that children with autism benefit from e...

Machine learning for outcome prediction in patients with non-valvular atrial fibrillation from the GLORIA-AF registry.

Clinical risk scores that predict outcomes in patients with atrial fibrillation (AF) have modest pre...

Multi-label text classification via secondary use of large clinical real-world data sets.

Procedural coding presents a taxing challenge for clinicians. However, recent advances in natural la...

Non-small cell lung cancer detection through knowledge distillation approach with teaching assistant.

Non-small cell lung cancer (NSCLC) exhibits a comparatively slower rate of metastasis in contrast to...

Enhanced NSCLC subtyping and staging through attention-augmented multi-task deep learning: A novel diagnostic tool.

OBJECTIVES: The objective of this study is to develop a novel multi-task learning approach with atte...

A Deep Learning Model to Predict Breast Implant Texture Types Using Ultrasonography Images: Feasibility Development Study.

BACKGROUND: Breast implants, including textured variants, have been widely used in aesthetic and rec...

Evaluating machine learning model bias and racial disparities in non-small cell lung cancer using SEER registry data.

BACKGROUND: Despite decades of pursuing health equity, racial and ethnic disparities persist in heal...

A deep learning framework for hepatocellular carcinoma diagnosis using MS1 data.

Clinical proteomics analysis is of great significance for analyzing pathological mechanisms and disc...

Novel large empirical study of deep transfer learning for COVID-19 classification based on CT and X-ray images.

The early and highly accurate prediction of COVID-19 based on medical images can speed up the diagno...

A survey on representation learning for multi-view data.

Multi-view clustering has become a rapidly growing field in machine learning and data mining areas b...

Deep learning-based automatic image classification of oral cancer cells acquiring chemoresistance in vitro.

Cell shape reflects the spatial configuration resulting from the equilibrium of cellular and environ...

Machine learning models reveal ARHGAP11A's impact on lymph node metastasis and stemness in NSCLC.

Most patients with non-small cell lung cancer (NSCLC) are diagnosed at an advanced stage of the dise...

Identification of ferroptosis-related genes associated with diffuse large B-cell lymphoma via bioinformatics and machine learning approaches.

Ferroptosis has emerged as a critical mechanism in the development and progression of various tumors...

Innovative label-free lymphoma diagnosis using infrared spectroscopy and machine learning on tissue sections.

The diagnosis of lymphomas is challenging due to their diverse histological presentations and clinic...

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