Oncology/Hematology

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

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Showing 2017-2037 of 15,280 articles
Predicting Clinical Anticancer Drug Response of Patients by Using Domain Alignment and Prototypical Learning.

Anticancer drug response prediction is crucial in developing personalized treatment plans for cancer...

An assessment of breast cancer HER2, ER, and PR expressions based on mammography using deep learning with convolutional neural networks.

Mammography is the recommended imaging modality for breast cancer screening. Expressions of human ep...

Next-generation sequencing based deep learning model for prediction of HER2 status and response to HER2-targeted neoadjuvant chemotherapy.

INTRODUCTION: For patients with breast cancer, the amplification of Human Epidermal Growth Factor 2 ...

Assessing multiple MRI sequences in deep learning-based synthetic CT generation for MR-only radiation therapy of head and neck cancers.

PURPOSE: This study investigated the effect of multiple magnetic resonance (MR) sequences on the qua...

Adaptive genetic algorithm based deep feature selector for cancer detection in lung histopathological images.

Cancer is a global health concern because of a significant mortality rate and a wide range of affect...

Unravelling single-cell DNA replication timing dynamics using machine learning reveals heterogeneity in cancer progression.

Genomic heterogeneity has largely been overlooked in single-cell replication timing (scRT) studies. ...

A Hybrid Machine Learning CT-Based Radiomics Nomogram for Predicting Cancer-Specific Survival in Curatively Resected Colorectal Cancer.

RATIONALE AND OBJECTIVES: To develop and validate a computed tomography-based radiomics nomogram for...

Revolutionizing prostate cancer therapy: Artificial intelligence - Based nanocarriers for precision diagnosis and treatment.

Prostate cancer is one of the major health challenges in the world and needs novel therapeutic appro...

Integration of radiomic and deep features to reliably differentiate benign renal lesions from renal cell carcinoma.

PURPOSE: Accurate differentiation of benign renal lesions from renal cell carcinoma (RCC) is crucial...

A comprehensive analysis of deep learning and transfer learning techniques for skin cancer classification.

Accurately and early diagnosis of melanoma is one of the challenging tasks due to its unique charact...

Artificial intelligence with ChatGPT 4: a large language model in support of ocular oncology cases.

PURPOSE: To evaluate ChatGPT's ability to analyze comprehensive case descriptions of patients with u...

Enhancing deep learning methods for brain metastasis detection through cross-technique annotations on SPACE MRI.

BACKGROUND: Gadolinium-enhanced "sampling perfection with application-optimized contrasts using diff...

Multiple machine learning-based integrations of multi-omics data to identify molecular subtypes and construct a prognostic model for HNSCC.

BACKGROUND: Immunotherapy has introduced new breakthroughs in improving the survival of head and nec...

An Information Fusion System-Driven Deep Neural Networks With Application to Cancer Mortality Risk Estimate.

Next-generation sequencing (NGS) genomic data offer valuable high-throughput genomic information for...

Explainable Classification of Benign-Malignant Pulmonary Nodules With Neural Networks and Information Bottleneck.

Computerized tomography (CT) is a clinically primary technique to differentiate benign-malignant pul...

Machine learning identifies the association between second primary malignancies and postoperative radiotherapy in young-onset breast cancer patients.

BACKGROUND: A second primary malignant tumor is one of the most important factors affecting the long...

Class-aware multi-level attention learning for semi-supervised breast cancer diagnosis under imbalanced label distribution.

Breast cancer affects a significant number of patients worldwide, and early diagnosis is critical fo...

You get the best of both worlds? Integrating deep learning and traditional machine learning for breast cancer risk prediction.

Breast Cancer is the most commonly diagnosed cancer worldwide. While screening mammography diminishe...

Breast cancer classification based on hybrid CNN with LSTM model.

Breast cancer (BC) is a global problem, largely due to a shortage of knowledge and early detection. ...

Cross-ViT based benign and malignant classification of pulmonary nodules.

The benign and malignant discrimination of pulmonary nodules plays a very important role in diagnosi...

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