AIMC Topic: Machine Learning

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Data valuation for medical imaging using Shapley value and application to a large-scale chest X-ray dataset.

Scientific reports
The reliability of machine learning models can be compromised when trained on low quality data. Many large-scale medical imaging datasets contain low quality labels extracted from sources such as medical reports. Moreover, images within a dataset may...

Accurate cancer phenotype prediction with AKLIMATE, a stacked kernel learner integrating multimodal genomic data and pathway knowledge.

PLoS computational biology
Advancements in sequencing have led to the proliferation of multi-omic profiles of human cells under different conditions and perturbations. In addition, many databases have amassed information about pathways and gene "signatures"-patterns of gene ex...

The Predictive Value of Monocytes in Immune Microenvironment and Prognosis of Glioma Patients Based on Machine Learning.

Frontiers in immunology
Gliomas are primary malignant brain tumors. Monocytes have been proved to actively participate in tumor growth. Weighted gene co-expression network analysis was used to identify meaningful monocyte-related genes for clustering. Neural network and SVM...

Opportunities and Challenges in Democratizing Immunology Datasets.

Frontiers in immunology
The field of immunology is rapidly progressing toward a systems-level understanding of immunity to tackle complex infectious diseases, autoimmune conditions, cancer, and beyond. In the last couple of decades, advancements in data acquisition techniqu...

Predicting breast cancer 5-year survival using machine learning: A systematic review.

PloS one
BACKGROUND: Accurately predicting the survival rate of breast cancer patients is a major issue for cancer researchers. Machine learning (ML) has attracted much attention with the hope that it could provide accurate results, but its modeling methods a...

Predicting eyes at risk for rapid glaucoma progression based on an initial visual field test using machine learning.

PloS one
OBJECTIVE: To assess whether machine learning algorithms (MLA) can predict eyes that will undergo rapid glaucoma progression based on an initial visual field (VF) test.

Classification Criteria for Serpiginous Choroiditis.

American journal of ophthalmology
PURPOSE: To determine classification criteria for serpiginous choroiditis.