AIMC Topic: Deep Learning

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MRI-Based Deep Learning Segmentation and Radiomics of Sarcoma in Mice.

Tomography (Ann Arbor, Mich.)
Small-animal imaging is an essential tool that provides noninvasive, longitudinal insight into novel cancer therapies. However, considerable variability in image analysis techniques can lead to inconsistent results. We have developed quantitative ima...

Beluga whale acoustic signal classification using deep learning neural network models.

The Journal of the Acoustical Society of America
Over a decade after the Cook Inlet beluga (Delphinapterus leucas) was listed as endangered in 2008, the population has shown no sign of recovery. Lack of ecological knowledge limits the understanding of, and ability to manage, potential threats imped...

Restoring chaos using deep reinforcement learning.

Chaos (Woodbury, N.Y.)
A catastrophic bifurcation in non-linear dynamical systems, called crisis, often leads to their convergence to an undesirable non-chaotic state after some initial chaotic transients. Preventing such behavior has been quite challenging. We demonstrate...

Quantitative Assessment of the Effects of Compression on Deep Learning in Digital Pathology Image Analysis.

JCO clinical cancer informatics
PURPOSE: Deep learning (DL), a class of approaches involving self-learned discriminative features, is increasingly being applied to digital pathology (DP) images for tasks such as disease identification and segmentation of tissue primitives (eg, nucl...

DeepPod: a convolutional neural network based quantification of fruit number in Arabidopsis.

GigaScience
BACKGROUND: High-throughput phenotyping based on non-destructive imaging has great potential in plant biology and breeding programs. However, efficient feature extraction and quantification from image data remains a bottleneck that needs to be addres...

Deep learning-based quantitative visualization and measurement of extraperitoneal hematoma volumes in patients with pelvic fractures: Potential role in personalized forecasting and decision support.

The journal of trauma and acute care surgery
INTRODUCTION: Admission computed tomography (CT) is a widely used diagnostic tool for patients with pelvic fractures. In this pilot study, we hypothesized that pelvic hematoma volumes derived using a rapid automated deep learning-based quantitative v...

Low-Dose Abdominal CT Using a Deep Learning-Based Denoising Algorithm: A Comparison with CT Reconstructed with Filtered Back Projection or Iterative Reconstruction Algorithm.

Korean journal of radiology
OBJECTIVE: To compare the image quality of low-dose (LD) computed tomography (CT) obtained using a deep learning-based denoising algorithm (DLA) with LD CT images reconstructed with a filtered back projection (FBP) and advanced modeled iterative reco...

Research Techniques Made Simple: Deep Learning for the Classification of Dermatological Images.

The Journal of investigative dermatology
Deep learning is a branch of artificial intelligence that uses computational networks inspired by the human brain to extract patterns from raw data. Development and application of deep learning methods for image analysis, including classification, se...

Glaucoma screening: where are we and where do we need to go?

Current opinion in ophthalmology
PURPOSE OF REVIEW: Current recommendations for glaucoma screening are decidedly neutral. No studies have yet documented improved long-term outcomes for individuals who undergo glaucoma screening versus those who do not. Given the long duration that w...