Oncology/Hematology

Other Cancers

Latest AI and machine learning research in other cancers for healthcare professionals.

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Deep learning analysis using FDG-PET to predict treatment outcome in patients with oral cavity squamous cell carcinoma.

OBJECTIVE: To assess the utility of deep learning analysis using F-fluorodeoxyglucose (FDG) uptake b...

Assessment of liver metastases radiomic feature reproducibility with deep-learning-based semi-automatic segmentation software.

BACKGROUND: Good feature reproducibility enhances model reliability. The manual segmentation of gast...

Hematologist-Level Classification of Mature B-Cell Neoplasm Using Deep Learning on Multiparameter Flow Cytometry Data.

The wealth of information captured by multiparameter flow cytometry (MFC) can be analyzed by recent ...

Weakly-supervised learning for lung carcinoma classification using deep learning.

Lung cancer is one of the major causes of cancer-related deaths in many countries around the world, ...

A Framework for Effective Application of Machine Learning to Microbiome-Based Classification Problems.

Machine learning (ML) modeling of the human microbiome has the potential to identify microbial bioma...

An improved clear cell renal cell carcinoma stage prediction model based on gene sets.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcino...

Prospective Analysis Using a Novel CNN Algorithm to Distinguish Atypical Ductal Hyperplasia From Ductal Carcinoma in Situ in Breast.

INTRODUCTION: We previously developed a convolutional neural networks (CNN)-based algorithm to disti...

Automatic detection of cervical lymph nodes in patients with oral squamous cell carcinoma using a deep learning technique: a preliminary study.

OBJECTIVE: To apply a deep learning object detection technique to CT images for detecting cervical l...

Automated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning.

PURPOSE: Liver Imaging Reporting and Data System (LI-RADS) uses multiphasic contrast-enhanced imagin...

Deep learning for identification of critical regions associated with toxicities after liver stereotactic body radiation therapy.

PURPOSE: Radiation therapy (RT) is prescribed for curative and palliative treatment for around 50% o...

The challenges of colposcopy for cervical cancer screening in LMICs and solutions by artificial intelligence.

BACKGROUND: The World Health Organization (WHO) called for global action towards the elimination of ...

Prediction of N6-methyladenosine sites using convolution neural network model based on distributed feature representations.

N-methyladenosine (mA) is a well-studied and most common interior messenger RNA (mRNA) modification ...

Machine-learning based MRI radiomics models for early detection of radiation-induced brain injury in nasopharyngeal carcinoma.

BACKGROUND: Early radiation-induced temporal lobe injury (RTLI) diagnosis in nasopharyngeal carcinom...

Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm.

MOTIVATION: Brain or central nervous system cancer is the tenth leading cause of death in men and wo...

Deep learning shows the capability of high-level computer-aided diagnosis in malignant lymphoma.

A pathological evaluation is one of the most important methods for the diagnosis of malignant lympho...

Beyond the limitation of targeted therapy: Improve the application of targeted drugs combining genomic data with machine learning.

Precision oncology involves effectively selecting drugs for cancer patients and planning an effectiv...

Robot technology identifies a Parkinsonian therapeutics repurpose to target stem cells of glioblastoma.

Glioblastoma is a heterogeneous lethal disease, regulated by a stem-cell hierarchy and the neurotra...

Differentiation of Benign from Malignant Pulmonary Nodules by Using a Convolutional Neural Network to Determine Volume Change at Chest CT.

Background Deep learning may help to improve computer-aided detection of volume (CADv) measurement o...

Identification of benign and malignant pulmonary nodules on chest CT using improved 3D U-Net deep learning framework.

PURPOSE: To accurately distinguish benign from malignant pulmonary nodules with CT based on partial ...

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