Oncology/Hematology

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

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The Role of AI in the Evaluation of Neuroendocrine Tumors: Current State of the Art.

Advancements in Artificial Intelligence (AI) are driving a paradigm shift in the field of medical di...

ieGENES: A machine learning method for selecting differentially expressed genes in cancer studies.

Gene selection is crucial for cancer classification using microarray data. In the interests of impro...

GBCHV an advanced deep learning anatomy aware model for accurate classification of gallbladder cancer utilizing ultrasound images.

This study introduces a novel deep learning approach aimed at accurately classifying Gallbladder Can...

OnmiMHC: a machine learning solution for UCEC tumor vaccine development through enhanced peptide-MHC binding prediction.

The key roles of Major Histocompatibility Complex (MHC) Class I and II molecules in the immune syste...

Nonlocal models in biology and life sciences: Sources, developments, and applications.

Mathematical modeling is one of the fundamental techniques for understanding biophysical mechanisms ...

Deep learning-driven prediction in healthcare systems: Applying advanced CNNs for enhanced breast cancer detection.

The mortality risk associated with breast cancer is experiencing an exponential rise, underscoring t...

Living Microalgae-Based Magnetic Microrobots for Calcium Overload and Photodynamic Synergetic Cancer Therapy.

The combination of Ca overload and reactive oxygen species (ROS) production for cancer therapy offer...

Effectiveness of AI for Enhancing Computed Tomography Image Quality and Radiation Protection in Radiology: Systematic Review and Meta-Analysis.

BACKGROUND: Artificial intelligence (AI) presents a promising approach to balancing high image quali...

Predicting the efficacy of neoadjuvant chemotherapy in breast cancer patients based on ultrasound longitudinal temporal depth network fusion model.

OBJECTIVE: The aim of this study was to develop and validate a deep learning radiomics (DLR) model b...

Auxiliary meta-learning strategy for cancer recognition: leveraging external data and optimized feature mapping.

As reported by the International Agency for Research on Cancer (IARC), the global incidence of cance...

T1-weighted MRI-based brain tumor classification using hybrid deep learning models.

Health is fundamental to human well-being, with brain health particularly critical for cognitive fun...

Identification of potential biomarkers for lung cancer using integrated bioinformatics and machine learning approaches.

Lung cancer is one of the most common cancer and the leading cause of cancer-related death worldwide...

Artificial intelligence in endoscopic diagnosis of esophageal squamous cell carcinoma and precancerous lesions.

Esophageal squamous cell carcinoma (ESCC) poses a significant global health challenge, necessitating...

Evaluation of MRI anatomy in machine learning predictive models to assess hydrogel spacer benefit for prostate cancer patients.

INTRODUCTION: Hydrogel spacers (HS) are designed to minimise the radiation doses to the rectum in pr...

Pre-transplant and transplant parameters predict long-term survival after hematopoietic cell transplantation using machine learning.

BACKGROUND: Allogeneic hematopoietic stem transplantation (allo-HSCT) constitutes a curative treatme...

Machine Learning-Aided Intelligent Monitoring of Multivariate miRNA Biomarkers Using Bipolar Self-powered Sensors.

Breast cancer has become the most prevalent form of cancer among women on a global scale. The early ...

Untargeted Lipidomic Biomarkers for Liver Cancer Diagnosis: A Tree-Based Machine Learning Model Enhanced by Explainable Artificial Intelligence.

: Liver cancer ranks among the leading causes of cancer-related mortality, necessitating the develop...

Using deep learning to differentiate among histology renal tumor types in computed tomography scans.

BACKGROUND: This study employed a convolutional neural network (CNN) to analyze computed tomography ...

Enhanced glioma tumor detection and segmentation using modified deep learning with edge fusion and frequency features.

Computer-aided automatic brain tumor detection is crucial for timely diagnosis and treatment, especi...

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