Oncology/Hematology

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

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Showing 1366-1386 of 15,250 articles
Reduction of radiation exposure in chest radiography using deep learning-based noise reduction processing: A phantom and retrospective clinical study.

INTRODUCTION: Intelligent noise reduction (INR), a deep learning-based noise reduction developed by ...

Leveraging TME features and multi-omics data with an advanced deep learning framework for improved Cancer survival prediction.

Glioma, a malignant intracranial tumor with high invasiveness and heterogeneity, significantly impac...

Variational mode directed deep learning framework for breast lesion classification using ultrasound imaging.

Breast cancer is the most prevalent cancer and the second cause of cancer related death among women ...

Role of artificial intelligence in advancing immunology.

Artificial intelligence (AI) has revolutionized various biomedical fields, particularly immunology, ...

Personalized therapeutic strategies and prognosis for advanced laryngeal squamous cell carcinoma: Insights from machine learning models.

PURPOSE: Despite the development of diverse treatment options, there has been an increase in mortali...

Options for postoperative radiation therapy in patients with de novo metastatic breast cancer.

BACKGROUND: Although meta-analyses have demonstrated survival benefits associated with primary tumor...

Development and validation of an interpretable machine learning model for diagnosing pathologic complete response in breast cancer.

BACKGROUND: Pathologic complete response (pCR) following neoadjuvant chemotherapy (NACT) is a critic...

Unsupervised non-small cell lung cancer tumor segmentation using cycled generative adversarial network with similarity-based discriminator.

BACKGROUND: Tumor segmentation is crucial for lung disease diagnosis and treatment. Most existing de...

The predictive role of identifying frailty in assessing the need for palliative care in the elderly: the application of machine learning algorithm.

BACKGROUND: Palliative care is a key component of integrated care to improve care quality and reduce...

Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges.

Drug resistance is one of the main reasons for cancer treatment failure, leading to a rapid recurren...

Incorporation of explainable artificial intelligence in ensemble machine learning-driven pancreatic cancer diagnosis.

Despite the strides made in medical science, pancreatic cancer continues to be a threat, highlightin...

Bald eagle-optimized transformer networks with temporal-spatial mid-level features for pancreatic tumor classification.

The classification and diagnosis of pancreatic tumors present significant challenges due to their in...

Constructing a neural network model based on tumor-infiltrating lymphocytes (TILs) to predict the survival of hepatocellular carcinoma patients.

BACKGROUND: Hepatocellular carcinoma (HCC) is the most common primary liver cancer worldwide, and ea...

The establishment of machine learning prognostic prediction models for pineal region tumors based on SEER-A multicenter real-world study.

BACKGROUND: Pineal region tumors (PRT) are rare intracranial neoplasms with diverse pathological typ...

EfficientNet-Based Attention Residual U-Net With Guided Loss for Breast Tumor Segmentation in Ultrasound Images.

OBJECTIVE: Breast cancer poses a major health concern for women globally. Effective segmentation of ...

Bridging surgical oncology and personalized medicine: the role of artificial intelligence and machine learning in thoracic surgery.

Lung cancer remains the leading cause of cancer-related deaths globally, often detected in advanced ...

Clinical performance of a machine learning-based model for detecting lymph node metastasis in papillary thyroid carcinoma: A multicenter study.

Papillary thyroid carcinoma (PTC) is a common endocrine malignancy with a generally favorable progno...

A comparative analysis of three graph neural network models for predicting axillary lymph node metastasis in early-stage breast cancer.

The presence of axillary lymph node metastasis (ALNM) in breast cancer patients is an important fact...

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