AIMC Topic: Image Processing, Computer-Assisted

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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 ...

Information-Based Boundary Equilibrium Generative Adversarial Networks with Interpretable Representation Learning.

Computational intelligence and neuroscience
This paper describes a new image generation algorithm based on generative adversarial network. With an information-theoretic extension to the autoencoder-based discriminator, this new algorithm is able to learn interpretable representations from the ...

Generative Adversarial Network for Medical Images (MI-GAN).

Journal of medical systems
Deep learning algorithms produces state-of-the-art results for different machine learning and computer vision tasks. To perform well on a given task, these algorithms require large dataset for training. However, deep learning algorithms lack generali...

Retinal image quality assessment using deep learning.

Computers in biology and medicine
Poor-quality retinal images do not allow an accurate medical diagnosis, and it is inconvenient for a patient to return to a medical center to repeat the fundus photography exam. In this paper, a robust automatic system is proposed to assess the quali...

Deep Learning to Distinguish Recalled but Benign Mammography Images in Breast Cancer Screening.

Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE: False positives in digital mammography screening lead to high recall rates, resulting in unnecessary medical procedures to patients and health care costs. This study aimed to investigate the revolutionary deep learning methods to distinguish...

Multimodal MRI-based classification of migraine: using deep learning convolutional neural network.

Biomedical engineering online
BACKGROUND: Recently, deep learning technologies have rapidly expanded into medical image analysis, including both disease detection and classification. As far as we know, migraine is a disabling and common neurological disorder, typically characteri...

Artificial intelligence in healthcare.

Nature biomedical engineering
Artificial intelligence (AI) is gradually changing medical practice. With recent progress in digitized data acquisition, machine learning and computing infrastructure, AI applications are expanding into areas that were previously thought to be only t...

STRAINet: Spatially Varying sTochastic Residual AdversarIal Networks for MRI Pelvic Organ Segmentation.

IEEE transactions on neural networks and learning systems
Accurate segmentation of pelvic organs is important for prostate radiation therapy. Modern radiation therapy starts to use a magnetic resonance image (MRI) as an alternative to computed tomography image because of its superior soft tissue contrast an...

Accuracy of an artificial neural network for detecting a regional abnormality in myocardial perfusion SPECT.

Annals of nuclear medicine
OBJECTIVES: The patient-based diagnosis with an artificial neural network (ANN) has shown potential utility for the detection of coronary artery disease; however, the region-based accuracy of the detected regions has not been fully evaluated. The aim...