AIMC Topic: Deep Learning

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lncRNA_Mdeep: An Alignment-Free Predictor for Distinguishing Long Non-Coding RNAs from Protein-Coding Transcripts by Multimodal Deep Learning.

International journal of molecular sciences
Long non-coding RNAs (lncRNAs) play crucial roles in diverse biological processes and human complex diseases. Distinguishing lncRNAs from protein-coding transcripts is a fundamental step for analyzing the lncRNA functional mechanism. However, the exp...

: deep learning-based radiomics for the time-to-event outcome prediction in lung cancer.

Scientific reports
Hand-crafted radiomics has been used for developing models in order to predict time-to-event clinical outcomes in patients with lung cancer. Hand-crafted features, however, are pre-defined and extracted without taking the desired target into account....

Deep Learning Approach for Imputation of Missing Values in Actigraphy Data: Algorithm Development Study.

JMIR mHealth and uHealth
BACKGROUND: Data collected by an actigraphy device worn on the wrist or waist can provide objective measurements for studies related to physical activity; however, some data may contain intervals where values are missing. In previous studies, statist...

Label-Free Quantification of Pharmacokinetics in Skin with Stimulated Raman Scattering Microscopy and Deep Learning.

The Journal of investigative dermatology
The treatment of inflammatory skin conditions relies on a deep understanding of how drugs and tissue behave and interact. Although numerous methods have been developed that aim to follow and quantify topical drug pharmacokinetics, these tools can com...

Accelerating cardiac cine MRI using a deep learning-based ESPIRiT reconstruction.

Magnetic resonance in medicine
PURPOSE: To propose a novel combined parallel imaging and deep learning-based reconstruction framework for robust reconstruction of highly accelerated 2D cardiac cine MRI data.

Deep learning for the rapid automatic quantification and characterization of rotator cuff muscle degeneration from shoulder CT datasets.

European radiology
OBJECTIVES: This study aimed at developing a convolutional neural network (CNN) able to automatically quantify and characterize the level of degeneration of rotator cuff (RC) muscles from shoulder CT images including muscle atrophy and fatty infiltra...

Development and Validation of a Deep-learning Model to Assist With Renal Cell Carcinoma Histopathologic Interpretation.

Urology
OBJECTIVE: To develop and test the ability of a convolutional neural network (CNN) to accurately identify the presence of renal cell carcinoma (RCC) on histopathology specimens, as well as differentiate RCC histologic subtype and grade.

Needle tip force estimation by deep learning from raw spectral OCT data.

International journal of computer assisted radiology and surgery
PURPOSE: Needle placement is a challenging problem for applications such as biopsy or brachytherapy. Tip force sensing can provide valuable feedback for needle navigation inside the tissue. For this purpose, fiber-optical sensors can be directly inte...