AIMC Topic: Deep Learning

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Deep reinforcement learning stock market trading, utilizing a CNN with candlestick images.

PloS one
Billions of dollars are traded automatically in the stock market every day, including algorithms that use neural networks, but there are still questions regarding how neural networks trade. The black box nature of a neural network gives pause to entr...

MSPM: A modularized and scalable multi-agent reinforcement learning-based system for financial portfolio management.

PloS one
Financial portfolio management (PM) is one of the most applicable problems in reinforcement learning (RL) owing to its sequential decision-making nature. However, existing RL-based approaches rarely focus on scalability or reusability to adapt to the...

Fully automated intracardiac 4D flow MRI post-processing using deep learning for biventricular segmentation.

European radiology
OBJECTIVES: 4D flow MRI allows for a comprehensive assessment of intracardiac blood flow, useful for assessing cardiovascular diseases, but post-processing requires time-consuming ventricular segmentation throughout the cardiac cycle and is prone to ...

Interpretable tumor differentiation grade and microsatellite instability recognition in gastric cancer using deep learning.

Laboratory investigation; a journal of technical methods and pathology
Gastric cancer possesses great histological and molecular diversity, which creates obstacles for rapid and efficient diagnoses. Classic diagnoses either depend on the pathologist's judgment, which relies heavily on subjective experience, or time-cons...

Pollutant specific optimal deep learning and statistical model building for air quality forecasting.

Environmental pollution (Barking, Essex : 1987)
Poor air quality is becoming a critical environmental concern in different countries over the last several years. Most of the air pollutants have serious consequences on human health and wellbeing. In this context, efficient forecasting of air pollut...

A back-projection-and-filtering-like (BPF-like) reconstruction method with the deep learning filtration from listmode data in TOF-PET.

Medical physics
PURPOSE: The time-of-flight (TOF) information improves signal-to-noise ratio (SNR) for positron emission tomography (PET) imaging. Existing analytical algorithms for TOF PET usually follow a filtered back-projection process on reconstructing images f...

Deep learning-based diagnosis models for onychomycosis in dermoscopy.

Mycoses
BACKGROUND: Onychomycosis is a common disease. Emerging noninvasive, real-time techniques such as dermoscopy and deep convolutional neural networks have been proposed for the diagnosis of onychomycosis. However, deep learning application in dermoscop...

Automated 3D Fetal Brain Segmentation Using an Optimized Deep Learning Approach.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: MR imaging provides critical information about fetal brain growth and development. Currently, morphologic analysis primarily relies on manual segmentation, which is time-intensive and has limited repeatability. This work aimed...

A Robust Deep-Learning Model for Landslide Susceptibility Mapping: A Case Study of Kurdistan Province, Iran.

Sensors (Basel, Switzerland)
We mapped landslide susceptibility in Kamyaran city of Kurdistan Province, Iran, using a robust deep-learning (DP) model based on a combination of extreme learning machine (ELM), deep belief network (DBN), back propagation (BP), and genetic algorithm...

A Method for Pipeline Leak Detection Based on Acoustic Imaging and Deep Learning.

Sensors (Basel, Switzerland)
This paper proposes a reliable technique for pipeline leak detection using acoustic emission signals. The acoustic emission signal of a pipeline contains leak-related information. However, the noise in the signal often obscures the leak-related infor...