Oncology/Hematology

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

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A Deep Learning Model to Predict Breast Implant Texture Types Using Ultrasonography Images: Feasibility Development Study.

BACKGROUND: Breast implants, including textured variants, have been widely used in aesthetic and rec...

Machine learning models including patient-reported outcome data in oncology: a systematic literature review and analysis of their reporting quality.

PURPOSE: To critically examine the current state of machine learning (ML) models including patient-r...

Convolutional neural network-based classification of craniosynostosis and suture lines from multi-view cranial X-rays.

Early and precise diagnosis of craniosynostosis (CSO), which involves premature fusion of cranial su...

An improved AlexNet deep learning method for limb tumor cancer prediction and detection.

Synovial sarcoma (SS) is a rare cancer that forms in soft tissues around joints, and early detection...

Evaluating machine learning model bias and racial disparities in non-small cell lung cancer using SEER registry data.

BACKGROUND: Despite decades of pursuing health equity, racial and ethnic disparities persist in heal...

Construction of a Wilms tumor risk model based on machine learning and identification of cuproptosis-related clusters.

BACKGROUND: Cuproptosis, a recently identified type of programmed cell death triggered by copper, ha...

A deep learning framework for hepatocellular carcinoma diagnosis using MS1 data.

Clinical proteomics analysis is of great significance for analyzing pathological mechanisms and disc...

Utilizing machine learning and molecular dynamics for enhanced drug delivery in nanoparticle systems.

Materials data science and machine learning (ML) are pivotal in advancing cancer treatment strategie...

Identification of novel markers for neuroblastoma immunoclustering using machine learning.

BACKGROUND: Due to the unique heterogeneity of neuroblastoma, its treatment and prognosis are closel...

Automated Detection of Oral Malignant Lesions Using Deep Learning: Scoping Review and Meta-Analysis.

OBJECTIVE: Oral diseases, specifically malignant lesions, are serious global health concerns requiri...

Integrated machine learning to predict the prognosis of lung adenocarcinoma patients based on SARS-COV-2 and lung adenocarcinoma crosstalk genes.

Viruses are widely recognized to be intricately associated with both solid and hematological maligna...

Detection of carcinoembryonic antigen specificity using microwave biosensor with machine learning.

Early diagnosis and screening of tumor markers are essential for effective cancer treatment and impr...

Combining array-assisted SERS microfluidic chips and machine learning algorithms for clinical leukemia phenotyping.

The disease progression and treatment options of leukemia between different subtypes vary considerab...

Combination of plasma-based lipidomics and machine learning provides a useful diagnostic tool for ovarian cancer.

Ovarian cancer (OC), the second leading cause of death among gynecological cancers, is often diagnos...

FT-FEDTL: A fine-tuned feature-extracted deep transfer learning model for multi-class microwave-based brain tumor classification.

The microwave brain imaging (MBI) system is an emerging technology used to detect brain tumors in th...

Exploring patient stratification in head and neck squamous cell carcinoma using machine learning techniques: Preliminary results.

BACKGROUND: Head and Neck Squamous Cell Carcinoma (HNSCC) presents a significant challenge in oncolo...

A deep learning approach for ovarian cancer detection and classification based on fuzzy deep learning.

Different oncologists make their own decisions about the detection and classification of the type of...

HiDDEN: a machine learning method for detection of disease-relevant populations in case-control single-cell transcriptomics data.

In case-control single-cell RNA-seq studies, sample-level labels are transferred onto individual cel...

Enhancing MRI brain tumor classification: A comprehensive approach integrating real-life scenario simulation and augmentation techniques.

Brain cancer poses a significant global health challenge, with mortality rates showing a concerning ...

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