AIMC Topic: Deep Learning

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Automated deep learning in ophthalmology: AI that can build AI.

Current opinion in ophthalmology
PURPOSE OF REVIEW: The purpose of this review is to describe the current status of automated deep learning in healthcare and to explore and detail the development of these models using commercially available platforms. We highlight key studies demons...

Deep Learning-based Diagnosis of Glaucoma Using Wide-field Optical Coherence Tomography Images.

Journal of glaucoma
PURPOSE: (1) To evaluate the performance of deep learning (DL) classifier in detecting glaucoma, based on wide-field swept-source optical coherence tomography (SS-OCT) images. (2) To assess the performance of DL-based fusion methods in diagnosing gla...

Outcomes of Artificial Intelligence Volumetric Assessment of Kidneys and Renal Tumors for Preoperative Assessment of Nephron-Sparing Interventions.

Journal of endourology
Renal-cell carcinoma is the most common kidney cancer and the 13th most common cause of cancer death worldwide. Partial nephrectomy and percutaneous ablation, increasingly utilized to treat small renal masses and preserve renal parenchyma, require p...

[An arrhythmia classification method based on deep learning parallel network model].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University
OBJECTIVE: We propose a parallel neural network classification method to improve the performance of classification of 4 types of arrhythmias: normal beat, supraventricular ectopic beat, ventricular ectopic beat and fused beat.

Semantic image segmentation of brain MRI with deep learning.

Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences
OBJECTIVES: Previous studies on brain MRI image segmentation, such as threshold method, boundary detection method, and region method did not achieve good performance in complex scenes. Based on the deep learning segmentation technology, this study co...

Geometric deep learning of RNA structure.

Science (New York, N.Y.)
RNA molecules adopt three-dimensional structures that are critical to their function and of interest in drug discovery. Few RNA structures are known, however, and predicting them computationally has proven challenging. We introduce a machine learning...

Establishment and validation of a computer-assisted colonic polyp localization system based on deep learning.

World journal of gastroenterology
BACKGROUND: Artificial intelligence in colonoscopy is an emerging field, and its application may help colonoscopists improve inspection quality and reduce the rate of missed polyps and adenomas. Several deep learning-based computer-assisted detection...

Development and Evaluation of Deep Learning-based Automated Segmentation of Pituitary Adenoma in Clinical Task.

The Journal of clinical endocrinology and metabolism
CONTEXT: The resection plan of pituitary adenoma (PA) needs preoperative observation of the sellar region. Radiomics prediction requires high-quality segmentations. Manual delineation is time-consuming and subject to rater variability.

The attention schema theory in a neural network agent: Controlling visuospatial attention using a descriptive model of attention.

Proceedings of the National Academy of Sciences of the United States of America
In the attention schema theory (AST), the brain constructs a model of attention, the attention schema, to aid in the endogenous control of attention. Growing behavioral evidence appears to support the presence of a model of attention. However, a cent...

Auto-focusing and quantitative phase imaging using deep learning for the incoherent illumination microscopy system.

Optics express
It is well known that the quantitative phase information which is vital in the biomedical study is hard to be directly obtained with bright-field microscopy under incoherent illumination. In addition, it is impossible to maintain the living sample in...