Oncology/Hematology

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

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Predicting nodal metastases in papillary thyroid carcinoma using artificial intelligence.

BACKGROUND: The presence of nodal metastases is important in the treatment of papillary thyroid carc...

A CNN-based unified framework utilizing projection loss in unison with label noise handling for multiple Myeloma cancer diagnosis.

Multiple Myeloma (MM) is a malignancy of plasma cells. Similar to other forms of cancer, it demands ...

Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review.

Glioma is the most common primary intraparenchymal tumor of the brain and the 5-year survival rate o...

Learning deep features for dead and living breast cancer cell classification without staining.

Automated cell classification in cancer biology is a challenging topic in computer vision and machin...

A deep learning system to diagnose the malignant potential of urothelial carcinoma cells in cytology specimens.

BACKGROUND: Although deep learning algorithms for clinical cytology have recently been developed, th...

Deep learning for predicting COVID-19 malignant progression.

As COVID-19 is highly infectious, many patients can simultaneously flood into hospitals for diagnosi...

CancerSiamese: one-shot learning for predicting primary and metastatic tumor types unseen during model training.

BACKGROUND: The state-of-the-art deep learning based cancer type prediction can only predict cancer ...

Metrics to evaluate the performance of auto-segmentation for radiation treatment planning: A critical review.

Advances in artificial intelligence-based methods have led to the development and publication of num...

A Deep Learning Strategy for Automatic Sleep Staging Based on Two-Channel EEG Headband Data.

Sleep disturbances are common in Alzheimer's disease and other neurodegenerative disorders, and toge...

Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance.

Resistance to ionizing radiation, a first-line therapy for many cancers, is a major clinical challen...

A Visually Interpretable Deep Learning Framework for Histopathological Image-Based Skin Cancer Diagnosis.

Owing to the high incidence rate and the severe impact of skin cancer, the precise diagnosis of mali...

Application of artificial intelligence in gynecologic malignancies: A review.

With the development of machine learning and deep learning models, artificial intelligence is now be...

Integrating multi-omics data through deep learning for accurate cancer prognosis prediction.

BACKGROUND: Genomic information is nowadays widely used for precise cancer treatments. Since the ind...

Development and validation of MRI-based deep learning models for prediction of microsatellite instability in rectal cancer.

BACKGROUND: Microsatellite instability (MSI) predetermines responses to adjuvant 5-fluorouracil and ...

Totally robotic intracorporeal Monti-Yang continent ileovesicostomy in patient with previous robotic surgery-Technique description.

We present a video case report of a pediatric patient with previous robotic abdominal surgery who un...

Artificial intelligence in oncology: Path to implementation.

In recent years, the field of artificial intelligence (AI) in oncology has grown exponentially. AI s...

A combined microfluidic deep learning approach for lung cancer cell high throughput screening toward automatic cancer screening applications.

Lung cancer is a leading cause of cancer death in both men and women worldwide. The high mortality r...

EARN: an ensemble machine learning algorithm to predict driver genes in metastatic breast cancer.

BACKGROUND: Today, there are a lot of markers on the prognosis and diagnosis of complex diseases suc...

Cytotoxicity and cell imaging of six types of carbon nanodots prepared through carbonization and hydrothermal processing of natural plant materials.

In this study we prepared six types of carbon nanodots (CNDs) from natural plant materials - through...

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