AIMC Topic: Algorithms

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Peering Into the Black Box of Artificial Intelligence: Evaluation Metrics of Machine Learning Methods.

AJR. American journal of roentgenology
OBJECTIVE: Machine learning (ML) and artificial intelligence (AI) are rapidly becoming the most talked about and controversial topics in radiology and medicine. Over the past few years, the numbers of ML- or AI-focused studies in the literature have ...

Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy.

European journal of heart failure
AIMS: We tested the hypothesis that a machine learning (ML) algorithm utilizing both complex echocardiographic data and clinical parameters could be used to phenogroup a heart failure (HF) cohort and identify patients with beneficial response to card...

QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments.

PloS one
BACKGROUND: Over the past decade, machine learning techniques have revolutionized how research and science are done, from designing new materials and predicting their properties to data mining and analysis to assisting drug discovery to advancing cyb...

End-to-End syndrome differentiation of Yin deficiency and Yang deficiency in traditional Chinese medicine.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Yin and Yang, two concepts adapted from classical Chinese philosophy, play a diagnostic role in Traditional Chinese Medicine (TCM). The Yin and Yang in harmonious balance indicate health, whereas imbalances to either side in...

Using Kalman Filtering to Forecast Disease Trajectory for Patients With Normal Tension Glaucoma.

American journal of ophthalmology
PURPOSE: To determine whether a machine learning technique called Kalman filtering (KF) can accurately forecast future values of mean deviation (MD), pattern standard deviation, and intraocular pressure for patients with normal tension glaucoma (NTG)...

Unsupervised and real-time spike sorting chip for neural signal processing in hippocampal prosthesis.

Journal of neuroscience methods
BACKGROUND: Damage to the hippocampus will result in the loss of ability to form new long-term memories and cognitive disorders. At present, there is no effective medical treatment for this issue. Hippocampal cognitive prosthesis is proposed to repla...

Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation.

Radiology
Purpose To develop a deep learning (DL) algorithm to assess mammographic breast density. Materials and Methods In this retrospective study, a deep convolutional neural network was trained to assess Breast Imaging Reporting and Data System (BI-RADS) b...

CT image segmentation of bone for medical additive manufacturing using a convolutional neural network.

Computers in biology and medicine
BACKGROUND: The most tedious and time-consuming task in medical additive manufacturing (AM) is image segmentation. The aim of the present study was to develop and train a convolutional neural network (CNN) for bone segmentation in computed tomography...

An evaluation method of risk grades for prostate cancer using similarity measure of cubic hesitant fuzzy sets.

Journal of biomedical informatics
Prostate cancer (PC) is more common cancer in older men. Then, the existing evaluation method of PC risk grades is based on the AJCC (American Joint Committee on Cancer) staging/scoring system. It utilizes the comprehensive risk data of the prostate-...

Weighted contrastive divergence.

Neural networks : the official journal of the International Neural Network Society
Learning algorithms for energy based Boltzmann architectures that rely on gradient descent are in general computationally prohibitive, typically due to the exponential number of terms involved in computing the partition function. In this way one has ...