AIMC Topic: Machine Learning

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Automated classification of multiphoton microscopy images of ovarian tissue using deep learning.

Journal of biomedical optics
Histopathological image analysis of stained tissue slides is routinely used in tumor detection and classification. However, diagnosis requires a highly trained pathologist and can thus be time-consuming, labor-intensive, and potentially risk bias. He...

Machine Learning in Rehabilitation Assessment for Thermal and Heart Rate Data Processing.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Multimodal signal analysis based on sophisticated noninvasive sensors, efficient communication systems, and machine learning, have a rapidly increasing range of different applications. The present paper is devoted to pattern recognition and the analy...

An Adaptive Neural Spike Processor With Embedded Active Learning for Improved Unsupervised Sorting Accuracy.

IEEE transactions on biomedical circuits and systems
There is a need for integrated spike sorting processors in implantable devices with low power consumption that have improved accuracy. Learning the characteristics of the variable input neural signals and adapting the functionality of the sorting pro...

Learning-Based Compressive MRI.

IEEE transactions on medical imaging
In the area of magnetic resonance imaging (MRI), an extensive range of non-linear reconstruction algorithms has been proposed which can be used with general Fourier subsampling patterns. However, the design of these subsampling patterns has typically...

Image Reconstruction is a New Frontier of Machine Learning.

IEEE transactions on medical imaging
Over past several years, machine learning, or more generally artificial intelligence, has generated overwhelming research interest and attracted unprecedented public attention. As tomographic imaging researchers, we share the excitement from our imag...

Multisource Transfer Double DQN Based on Actor Learning.

IEEE transactions on neural networks and learning systems
Deep reinforcement learning (RL) comprehensively uses the psychological mechanisms of "trial and error" and "reward and punishment" in RL as well as powerful feature expression and nonlinear mapping in deep learning. Currently, it plays an essential ...

The New Possibilities from "Big Data" to Overlooked Associations Between Diabetes, Biochemical Parameters, Glucose Control, and Osteoporosis.

Current osteoporosis reports
PURPOSE OF REVIEW: To review current practices and technologies within the scope of "Big Data" that can further our understanding of diabetes mellitus and osteoporosis from large volumes of data. "Big Data" techniques involving supervised machine lea...

Collaborative and Reproducible Research: Goals, Challenges, and Strategies.

Journal of digital imaging
Combining imaging biomarkers with genomic and clinical phenotype data is the foundation of precision medicine research efforts. Yet, biomedical imaging research requires unique infrastructure compared with principally text-driven clinical electronic ...

Texture Analysis and Machine Learning for Detecting Myocardial Infarction in Noncontrast Low-Dose Computed Tomography: Unveiling the Invisible.

Investigative radiology
OBJECTIVES: The aim of this study was to test whether texture analysis and machine learning enable the detection of myocardial infarction (MI) on non-contrast-enhanced low radiation dose cardiac computed tomography (CCT) images.