AIMC Topic: Algorithms

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Machine learning evaluates changes in functional connectivity under a prolonged cognitive load.

Chaos (Woodbury, N.Y.)
One must be aware of the black-box problem by applying machine learning models to analyze high-dimensional neuroimaging data. It is due to a lack of understanding of the internal algorithms or the input features upon which most models make decisions ...

Classification of imbalanced oral cancer image data from high-risk population.

Journal of biomedical optics
SIGNIFICANCE: Early detection of oral cancer is vital for high-risk patients, and machine learning-based automatic classification is ideal for disease screening. However, current datasets collected from high-risk populations are unbalanced and often ...

Identification of glaucoma from fundus images using deep learning techniques.

Indian journal of ophthalmology
PURPOSE: Glaucoma is one of the preeminent causes of incurable visual disability and blindness across the world due to elevated intraocular pressure within the eyes. Accurate and timely diagnosis is essential for preventing visual disability. Manual ...

The Evolution of Image Reconstruction in PET: From Filtered Back-Projection to Artificial Intelligence.

PET clinics
PET can provide functional images revealing physiologic processes in vivo. Although PET has many applications, there are still some limitations that compromise its precision: the absorption of photons in the body causes signal attenuation; the dead-t...

A Brief History of AI: How to Prevent Another Winter (A Critical Review).

PET clinics
Artificial intelligence has witnessed exponential growth in the past decade. Advances in computing power and the design of sophisticated artificial intelligence algorithms have enabled computers to outperform humans in a variety of tasks. Yet, artifi...

A Web-Based Deep Learning Model for Automated Diagnosis of Otoscopic Images.

Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology
OBJECTIVES: To develop a multiclass-classifier deep learning model and website for distinguishing tympanic membrane (TM) pathologies based on otoscopic images.

Introduction to Artificial Intelligence and Machine Learning for Pathology.

Archives of pathology & laboratory medicine
CONTEXT.—: Recent developments in machine learning have stimulated intense interest in software that may augment or replace human experts. Machine learning may impact pathology practice by offering new capabilities in analysis, interpretation, and ou...

Corneal Edema Visualization With Optical Coherence Tomography Using Deep Learning: Proof of Concept.

Cornea
PURPOSE: Optical coherence tomography (OCT) is essential for the diagnosis and follow-up of corneal edema, but assessment can be challenging in minimal or localized edema. The objective was to develop and validate a novel automated tool to detect and...