AIMC Topic: Sample Size

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On the Rates of Convergence From Surrogate Risk Minimizers to the Bayes Optimal Classifier.

IEEE transactions on neural networks and learning systems
In classification, the use of 0-1 loss is preferable since the minimizer of 0-1 risk leads to the Bayes optimal classifier. However, due to the nonconvexity of 0-1 loss, this optimization problem is NP-hard. Therefore, many convex surrogate loss func...

Sample Size Calculation for Clinical Trials of Medical Decision Support Systems with Binary Outcome.

Sovremennye tekhnologii v meditsine
Currently, software products for use in medicine are actively developed. Among them, the dominant share belongs to clinical decision support systems (CDSS), which can be intelligent (based on mathematical models obtained by machine learning methods o...

Brain Functional Connectivity Analysis via Graphical Deep Learning.

IEEE transactions on bio-medical engineering
OBJECTIVE: Graphical deep learning models provide a desirable way for brain functional connectivity analysis. However, the application of current graph deep learning models to brain network analysis is challenging due to the limited sample size and c...

Not just "big" data: Importance of sample size, measurement error, and uninformative predictors for developing prognostic models for digital interventions.

Behaviour research and therapy
There is strong interest in developing a more efficient mental health care system. Digital interventions and predictive models of treatment prognosis will likely play an important role in this endeavor. This article reviews the application of popular...

A Cascade Flexible Neural Forest Model for Cancer Subtypes Classification on Gene Expression Data.

Computational intelligence and neuroscience
The correct classification of cancer subtypes is of great significance for the in-depth study of cancer pathogenesis and the realization of accurate treatment for cancer patients. In recent years, the classification of cancer subtypes using deep neur...

Deep learning methods for automatic segmentation of lower leg muscles and bones from MRI scans of children with and without cerebral palsy.

NMR in biomedicine
Cerebral palsy is a neurological condition that is known to affect muscle growth. Detailed investigations of muscle growth require segmentation of muscles from MRI scans, which is typically done manually. In this study, we evaluated the performance o...

The impact of training sample size on deep learning-based organ auto-segmentation for head-and-neck patients.

Physics in medicine and biology
To investigate the impact of training sample size on the performance of deep learning-based organ auto-segmentation for head-and-neck cancer patients, a total of 1160 patients with head-and-neck cancer who received radiotherapy were enrolled in this ...

Embryo selection with artificial intelligence: how to evaluate and compare methods?

Journal of assisted reproduction and genetics
Embryo selection within in vitro fertilization (IVF) is the process of evaluating qualities of fertilized oocytes (embryos) and selecting the best embryo(s) available within a patient cohort for subsequent transfer or cryopreservation. In recent year...