AIMC Topic: Deep Learning

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Automated Amharic News Categorization Using Deep Learning Models.

Computational intelligence and neuroscience
For decades, machine learning techniques have been used to process Amharic texts. The potential application of deep learning on Amharic document classification has not been exploited due to a lack of language resources. In this paper, we present a de...

Deep Learning in the Detection of Rare Fractures - Development of a "Deep Learning Convolutional Network" Model for Detecting Acetabular Fractures.

Zeitschrift fur Orthopadie und Unfallchirurgie
BACKGROUND: Fracture detection by artificial intelligence and especially Deep Convolutional Neural Networks (DCNN) is a topic of growing interest in current orthopaedic and radiological research. As learning a DCNN usually needs a large amount of tra...

SuperDTI: Ultrafast DTI and fiber tractography with deep learning.

Magnetic resonance in medicine
PURPOSE: To develop a deep learning-based reconstruction framework for ultrafast and robust diffusion tensor imaging and fiber tractography.

Classification of large-scale image database of various skin diseases using deep learning.

International journal of computer assisted radiology and surgery
PURPOSE: The purpose of this study was to develop a deep learning-based computer-aided diagnosis system for skin disease classification using photographic images of patients. The targets are 59 skin diseases, including localized and diffuse diseases ...

Deep-learning power and perspectives for genomic selection.

The plant genome
Deep learning (DL) is revolutionizing the development of artificial intelligence systems. For example, before 2015, humans were better than artificial machines at classifying images and solving many problems of computer vision (related to object loca...

Identification of Tissue of Origin and Guided Therapeutic Applications in Cancers of Unknown Primary Using Deep Learning and RNA Sequencing (TransCUPtomics).

The Journal of molecular diagnostics : JMD
Cancers of unknown primary (CUP) are metastatic cancers for which the primary tumor is not found despite thorough diagnostic investigations. Multiple molecular assays have been proposed to identify the tissue of origin (TOO) and inform clinical care;...

Deep learning for segmentation in radiation therapy planning: a review.

Journal of medical imaging and radiation oncology
Segmentation of organs and structures, as either targets or organs-at-risk, has a significant influence on the success of radiation therapy. Manual segmentation is a tedious and time-consuming task for clinicians, and inter-observer variability can a...

A Deep Learning-Based Automatic First-Arrival Picking Method for Ultrasound Sound-Speed Tomography.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Ultrasound sound-speed tomography (USST) has shown great prospects for breast cancer diagnosis due to its advantages of nonradiation, low cost, 3-D breast images, and quantitative indicators. However, the reconstruction quality of USST is highly depe...

Deep radiomics-based survival prediction in patients with chronic obstructive pulmonary disease.

Scientific reports
Heterogeneous clinical manifestations and progression of chronic obstructive pulmonary disease (COPD) affect patient health risk assessment, stratification, and management. Pulmonary function tests are used to diagnose and classify the severity of CO...

Self-supervised deep learning model for COVID-19 lung CT image segmentation highlighting putative causal relationship among age, underlying disease and COVID-19.

Journal of translational medicine
BACKGROUND: Coronavirus disease 2019 (COVID-19) is very contagious. Cases appear faster than the available Polymerase Chain Reaction test kits in many countries. Recently, lung computerized tomography (CT) has been used as an auxiliary COVID-19 testi...