Oncology/Hematology

Latest AI and machine learning research in oncology/hematology for healthcare professionals.

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Showing 232-252 of 15,190 articles
MSPO: A machine learning hyperparameter optimization method for enhanced breast cancer image classification.

As one of the major threats to women's health worldwide, breast cancer requires early diagnosis and ...

Effect of arc length on the deep learning prediction of monitor units in lung stereotactic ablative radiation therapy treatment.

INTRODUCTION: The dose magnitude required to fine-tune radiation in multi-lesion stereotactic ablati...

A novel hybrid convolutional and transformer network for lymphoma classification.

Lymphoma poses a critical health challenge worldwide, demanding computer aided solutions towards dia...

2.5D Deep Learning-Based Prediction of Pathological Grading of Clear Cell Renal Cell Carcinoma Using Contrast-Enhanced CT: A Multicenter Study.

RATIONALE AND OBJECTIVES: To develop and validate a deep learning model based on arterial phase-enha...

Emerging Role of MRI-Based Artificial Intelligence in Individualized Treatment Strategies for Hepatocellular Carcinoma: A Narrative Review.

Hepatocellular carcinoma (HCC) is the most common subtype of primary liver cancer, with significant ...

Enhancing breast cancer classification using a deep sparse wavelet autoencoder approach.

As digital imaging technology advances, accurate classification of 2D breast cancer images becomes i...

Risk assessment and interventions for malignant ground-glass lung nodules.

Pulmonary ground-glass nodules (GGNs) have become a major problem owing to their high frequency, lim...

Artificial Intelligence for Tumor [F]FDG PET Imaging: Advancements and Future Trends - Part II.

The integration of artificial intelligence (AI) into [F]FDG PET/CT imaging continues to expand, offe...

Artificial intelligence on inflammatory dermatoses: where we are and where are we going?

BACKGROUND: Artificial intelligence (AI) is increasingly gaining ground in dermatology, with studies...

Collaborative assessment of the risk of postoperative progression in early-stage non-small cell lung cancer: a robust federated learning model.

BACKGROUND: While the TNM staging system provides valuable insights into the extent of disease, pred...

STHD: probabilistic cell typing of single spots in whole transcriptome spatial data with high definition.

Recent advances in spatial transcriptomics technologies have enabled gene expression profiling acros...

Machine learning and discriminant analysis model for predicting benign and malignant pulmonary nodules.

BACKGROUND: Pulmonary Nodules (PNs) are a trend considered as the early manifestation of lung cancer...

Using machine learning to discover DNA metabolism biomarkers that direct prostate cancer treatment.

DNA metabolism genes play pivotal roles in the regulation of cellular processes that contribute to c...

Deep learning-based ultrasound diagnostic model for follicular thyroid carcinoma.

OBJECTIVES: It is challenging to preoperatively diagnose follicular thyroid carcinoma (FTC) on ultra...

Determination of lung cancer exhaled breath biomarkers using machine learning-a new analysis framework.

Exhaled breath samples of lung cancer patients (LC), tuberculosis (TB) patients and asymptomatic con...

Performance of Machine Learning in Diagnosing KRAS (Kirsten Rat Sarcoma) Mutations in Colorectal Cancer: Systematic Review and Meta-Analysis.

BACKGROUND: With the widespread application of machine learning (ML) in the diagnosis and treatment ...

Image-based inference of tumor cell trajectories enables large-scale cancer progression analysis.

Current approaches to estimating cell trajectories, tumor progression dynamics, and cell population ...

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