AIMC Topic: Algorithms

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An introduction to machine learning for classification and prediction.

Family practice
Classification and prediction tasks are common in health research. With the increasing availability of vast health data repositories (e.g. electronic medical record databases) and advances in computing power, traditional statistical approaches are be...

Deep Learning in Population Genetics.

Genome biology and evolution
Population genetics is transitioning into a data-driven discipline thanks to the availability of large-scale genomic data and the need to study increasingly complex evolutionary scenarios. With likelihood and Bayesian approaches becoming either intra...

An Arbitrary Scale Super-Resolution Approach for 3D MR Images via Implicit Neural Representation.

IEEE journal of biomedical and health informatics
High Resolution (HR) medical images provide rich anatomical structure details to facilitate early and accurate diagnosis. In magnetic resonance imaging (MRI), restricted by hardware capacity, scan time, and patient cooperation ability, isotropic 3-di...

An Area- and Energy-Efficient Spiking Neural Network With Spike-Time-Dependent Plasticity Realized With SRAM Processing-in-Memory Macro and On-Chip Unsupervised Learning.

IEEE transactions on biomedical circuits and systems
In this article, we present a spiking neural network (SNN) based on both SRAM processing-in-memory (PIM) macro and on-chip unsupervised learning with Spike-Time-Dependent Plasticity (STDP). Co-design of algorithm and hardware for hardware-friendly SN...

Image Quality Assessment of Deep Learning Image Reconstruction in Torso Computed Tomography Using Tube Current Modulation.

Acta medica Okayama
Novel deep learning image reconstruction (DLIR) reportedly changes the image quality characteristics based on object contrast and image noise. In clinical practice, computed tomography image noise is usually controlled by tube current modulation (TCM...

PyVisualFields: A Python Package for Visual Field Analysis.

Translational vision science & technology
PURPOSE: Artificial intelligence (AI) methods are changing all areas of research and have a variety of capabilities of analysis in ophthalmology, specifically in visual fields (VFs) to detect or predict vision loss progression. Whereas most of the AI...

Editorial Commentary: Knowledge is Power: A Primer for Machine Learning.

Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association
Machine learning (ML) has become an increasingly common statistical methodology in medical research. In recent years, ML techniques have been used with greater frequency to evaluate orthopaedic data. ML allows for the creation of adaptive predictive ...