AIMC Topic: Deep Learning

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A Deep Learning Workflow for Mass-Forming Intrahepatic Cholangiocarcinoma and Hepatocellular Carcinoma Classification Based on MRI.

Current oncology (Toronto, Ont.)
OBJECTIVE: Precise classification of mass-forming intrahepatic cholangiocarcinoma (MF-ICC) and hepatocellular carcinoma (HCC) based on magnetic resonance imaging (MRI) is crucial for personalized treatment strategy. The purpose of the present study w...

Smartwatch Sensors with Deep Learning to Predict the Purchase Intentions of Online Shoppers.

Sensors (Basel, Switzerland)
In the past decade, the scale of e-commerce has continued to grow. With the outbreak of the COVID-19 epidemic, brick-and-mortar businesses have been actively developing online channels where precision marketing has become the focus. This study propos...

Wireless Link Selection Methods for Maritime Communication Access Networks-A Deep Learning Approach.

Sensors (Basel, Switzerland)
In recent years, we have been witnessing a growing interest in the subject of communication at sea. One of the promising solutions to enable widespread access to data transmission capabilities in coastal waters is the possibility of employing an on-s...

Next generation insect taxonomic classification by comparing different deep learning algorithms.

PloS one
Insect taxonomy lies at the heart of many aspects of ecology, and identification tasks are challenging due to the enormous inter- and intraspecies variation of insects. Conventional methods used to study insect taxonomy are often tedious, time-consum...

Prognostic significance of pulmonary arterial wedge pressure estimated by deep learning in acute heart failure.

ESC heart failure
AIMS: Acute decompensated heart failure (ADHF) presents with pulmonary congestion, which is caused by an increased pulmonary arterial wedge pressure (PAWP). PAWP is strongly associated with prognosis, but its quantitative evaluation is often difficul...

On joint parameterizations of linear and nonlinear functionals in neural networks.

Neural networks : the official journal of the International Neural Network Society
The paper proposes a new class of nonlinear operators and a dual learning paradigm where optimization jointly concerns both linear convolutional weights and the parameters of these nonlinear operators. The nonlinear class proposed to perform a rich f...

Technical performance of a dual-energy CT system with a novel deep-learning based reconstruction process: Evaluation using an abdomen protocol.

Medical physics
BACKGROUND: A new tube voltage-switching dual-energy (DE) CT system using a novel deep-learning based reconstruction process has been introduced. Characterizing the performance of this DE approach can help demonstrate its benefits and potential drawb...

Deep learning-based method for automatic resolution of gas chromatography-mass spectrometry data from complex samples.

Journal of chromatography. A
Modern gas chromatography-mass spectrometry (GC-MS) is the workhorse for the high-throughput profiling of volatile compounds in complex samples. It can produce a considerable amount of two-dimensional data, and automatic methods are required to disti...

Nystagmus patterns classification framework based on deep learning and optical flow.

Computers in biology and medicine
Benign paroxysmal positional vertigo (BPPV) is the most common vestibular peripheral vertigo disease characterized by brief recurrent vertigo with positional nystagmus. Clinically, it is common to recognize the patterns of nystagmus by analyzing infr...

High throughput optimization of medium composition for Escherichia coli protein expression using deep learning and Bayesian optimization.

Journal of bioscience and bioengineering
To improve synthetic media for protein expression in Escherichia coli, a strategy using deep neural networks (DNN) and Bayesian optimization was performed in this study. To obtain training data for a deep learning algorithm, E. coli harvesting a plas...