AIMC Topic: Deep Learning

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Prediction of lipomatous soft tissue malignancy on MRI: comparison between machine learning applied to radiomics and deep learning.

European radiology experimental
OBJECTIVES: Malignancy of lipomatous soft-tissue tumours diagnosis is suspected on magnetic resonance imaging (MRI) and requires a biopsy. The aim of this study is to compare the performances of MRI radiomic machine learning (ML) analysis with deep l...

Highly accelerated MR parametric mapping by undersampling the k-space and reducing the contrast number simultaneously with deep learning.

Physics in medicine and biology
To propose a novel deep learning-based method called RG-Net (reconstruction and generation network) for highly accelerated MR parametric mapping by undersampling k-space and reducing the acquired contrast number simultaneously.The proposed framework ...

Application of GIS Technology-Supported Cross Media Fusion Method Based on Deep Learning in Landscape Performance Evaluation.

Computational intelligence and neuroscience
GIS technology can provide reasonable and sustainable data support for landscape planning and ecological development and make wetland landscape planning consider the spatial layout of landscape and the optimal allocation of resources more. The key te...

Differentiating single cervical cells by mitochondrial fluorescence imaging and deep learning-based label-free light scattering with multi-modal static cytometry.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Cervical cancer is a high-risk disease that threatens women's health globally. In this study, we developed the multi-modal static cytometry that adopted different features to classify the typical human cervical epithelial cells (H8) and cervical canc...

HVIOnet: A deep learning based hybrid visual-inertial odometry approach for unmanned aerial system position estimation.

Neural networks : the official journal of the International Neural Network Society
Sensor fusion is used to solve the localization problem in autonomous mobile robotics applications by integrating complementary data acquired from various sensors. In this study, we adopt Visual-Inertial Odometry (VIO), a low-cost sensor fusion metho...

Non-small cell lung cancer diagnosis aid with histopathological images using Explainable Deep Learning techniques.

Computer methods and programs in biomedicine
BACKGROUND: Lung cancer has the highest mortality rate in the world, twice as high as the second highest. On the other hand, pathologists are overworked and this is detrimental to the time spent on each patient, diagnostic turnaround time, and their ...

Exploration of deep learning models for real-time monitoring of state and performance of anaerobic digestion with online sensors.

Bioresource technology
The immediate response to the state disturbances of anaerobic digestion is essential to prevent anaerobic digestion failure. However, frequent monitoring of the state and performance of anaerobic digestion is challenging. Thus, deep learning models w...

Artificial intelligence, machine learning and deep learning in musculoskeletal imaging: Current applications.

Journal of clinical ultrasound : JCU
Artificial intelligence is rapidly expanding in all technological fields. The medical field, and especially diagnostic imaging, has been showing the highest developmental potential. Artificial intelligence aims at human intelligence simulation throug...

Practical and Accurate Indoor Localization System Using Deep Learning.

Sensors (Basel, Switzerland)
Indoor localization is an important technology for providing various location-based services to smartphones. Among the various indoor localization technologies, pedestrian dead reckoning using inertial measurement units is a simple and highly practic...

Deep learning-based reconstruction on cardiac CT yields distinct radiomic features compared to iterative and filtered back projection reconstructions.

Scientific reports
We aimed to determine the effects of deep learning-based reconstruction (DLR) on radiomic features obtained from cardiac computed tomography (CT) by comparing with iterative reconstruction (IR), and filtered back projection (FBP). A total of 284 cons...