AIMC Topic: Deep Learning

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Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation.

IEEE transactions on neural networks and learning systems
A common shortfall of supervised deep learning for medical imaging is the lack of labeled data, which is often expensive and time consuming to collect. This article presents a new semisupervised method for medical image segmentation, where the networ...

A Survey of the Usages of Deep Learning for Natural Language Processing.

IEEE transactions on neural networks and learning systems
Over the last several years, the field of natural language processing has been propelled forward by an explosion in the use of deep learning models. This article provides a brief introduction to the field and a quick overview of deep learning archite...

Multitask Learning for Estimating Multitype Cardiac Indices in MRI and CT Based on Adversarial Reverse Mapping.

IEEE transactions on neural networks and learning systems
The estimation of multitype cardiac indices from cardiac magnetic resonance imaging (MRI) and computed tomography (CT) images attracts great attention because of its clinical potential for comprehensive function assessment. However, the most exiting ...

Dynamical Channel Pruning by Conditional Accuracy Change for Deep Neural Networks.

IEEE transactions on neural networks and learning systems
Channel pruning is an effective technique that has been widely applied to deep neural network compression. However, many existing methods prune from a pretrained model, thus resulting in repetitious pruning and fine-tuning processes. In this article,...

Deep Coattention-Based Comparator for Relative Representation Learning in Person Re-Identification.

IEEE transactions on neural networks and learning systems
Person re-identification (re-ID) favors discriminative representations over unseen shots to recognize identities in disjoint camera views. Effective methods are developed via pair-wise similarity learning to detect a fixed set of region features, whi...

Efficient Prediction of In Vitro Piroxicam Release and Diffusion From Topical Films Based on Biopolymers Using Deep Learning Models and Generative Adversarial Networks.

Journal of pharmaceutical sciences
The purpose of this study was to simultaneously predict the drug release and skin permeation of Piroxicam (PX) topical films based on Chitosan (CTS), Xanthan gum (XG) and its Carboxymethyl derivatives (CMXs) as matrix systems. These films were prepar...

An anatomical knowledge-based MRI deep learning pipeline for white matter hyperintensity quantification associated with cognitive impairment.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Recent studies have confirmed that white matter hyperintensities (WMHs) accumulated in strategic brain regions can predict cognitive impairments associated with Alzheimer's disease (AD). The knowledge of white matter anatomy facilitates lesion-sympto...

Six artificial intelligence paradigms for tissue characterisation and classification of non-COVID-19 pneumonia against COVID-19 pneumonia in computed tomography lungs.

International journal of computer assisted radiology and surgery
BACKGROUND: COVID-19 pandemic has currently no vaccines. Thus, the only feasible solution for prevention relies on the detection of COVID-19-positive cases through quick and accurate testing. Since artificial intelligence (AI) offers the powerful mec...

Large-Scale Modeling of Multispecies Acute Toxicity End Points Using Consensus of Multitask Deep Learning Methods.

Journal of chemical information and modeling
Computational methods to predict molecular properties regarding safety and toxicology represent alternative approaches to expedite drug development, screen environmental chemicals, and thus significantly reduce associated time and costs. There is a s...

Deep Learning-Based Annotation Transfer between Molecular Imaging Modalities: An Automated Workflow for Multimodal Data Integration.

Analytical chemistry
An ever-increasing array of imaging technologies are being used in the study of complex biological samples, each of which provides complementary, occasionally overlapping information at different length scales and spatial resolutions. It is important...