Oncology/Hematology

Other Cancers

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

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Glioma Tumor Grade Identification Using Artificial Intelligent Techniques.

Computer aided diagnosis using artificial intelligent techniques made tremendous improvement in medi...

Distinction between benign and malignant breast masses at breast ultrasound using deep learning method with convolutional neural network.

PURPOSE: We aimed to use deep learning with convolutional neural network (CNN) to discriminate betwe...

Predicting Ewing Sarcoma Treatment Outcome Using Infrared Spectroscopy and Machine Learning.

BACKGROUND: Improved outcome prediction is vital for the delivery of risk-adjusted, appropriate and ...

Gene Expression Classification of Lung Adenocarcinoma into Molecular Subtypes.

As one of the most common malignancies in the world, lung adenocarcinoma (LUAD) is currently difficu...

A novel end-to-end brain tumor segmentation method using improved fully convolutional networks.

Accurate brain magnetic resonance imaging (MRI) tumor segmentation continues to be an active researc...

Machine learning to predict occult nodal metastasis in early oral squamous cell carcinoma.

OBJECTIVES: To develop and validate an algorithm to predict occult nodal metastasis in clinically no...

Automatic Sleep Staging Employing Convolutional Neural Networks and Cortical Connectivity Images.

Understanding of the neuroscientific sleep mechanisms is associated with mental/cognitive and physic...

Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with CT.

PURPOSE: To develop a deep learning-based computer-aided diagnosis (CAD) system for use in the CT di...

Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers.

This study aimed to identify the optimal radiomic machine-learning classifier for differentiating gl...

Convolutional neural network for cell classification using microscope images of intracellular actin networks.

Automated cell classification is an important yet a challenging computer vision task with significan...

Sleep staging from single-channel EEG with multi-scale feature and contextual information.

PURPOSE: Portable sleep monitoring devices with less-attached sensors and high-accuracy sleep stagin...

Dual-energy CT for automatic organs-at-risk segmentation in brain-tumor patients using a multi-atlas and deep-learning approach.

In radiotherapy, computed tomography (CT) datasets are mostly used for radiation treatment planning ...

Random forest classifiers aid in the detection of incidental osteoblastic osseous metastases in DEXA studies.

PURPOSE: Dual-energy X-ray absorptiometry (DEXA) studies are used for screening patients for low bon...

Reinforcement learning-based control of tumor growth under anti-angiogenic therapy.

BACKGROUND AND OBJECTIVES: In recent decades, cancer has become one of the most fatal and destructiv...

Light-driven ultrasensitive self-powered cytosensing of circulating tumor cells via integration of biofuel cells and a photoelectrochemical strategy.

Herein, a light-driven, membrane-less and mediator-less self-powered cytosensing platform via integr...

Predicting one-year outcome in first episode psychosis using machine learning.

BACKGROUND: Early illness course correlates with long-term outcome in psychosis. Accurate prediction...

Detection of chromosome structural variation by targeted next-generation sequencing and a deep learning application.

Molecular testing is increasingly important in cancer diagnosis. Targeted next generation sequencing...

Identification of esophageal cancer pathway deviation and construction of a diagnosis model using three kernel genes.

The purpose of this study is to better understand the role of interleukin 35 (IL35) in esophageal ca...

Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks.

Classification of histologic patterns in lung adenocarcinoma is critical for determining tumor grade...

Natural Language Processing of Radiology Reports in Patients With Hepatocellular Carcinoma to Predict Radiology Resource Utilization.

OBJECTIVE: Radiology is a finite health care resource in high demand at most health centers. However...

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