AIMC Topic: Deep Learning

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Decoding rejuvenating effects of mechanical loading on skeletal aging using in vivo μCT imaging and deep learning.

Acta biomaterialia
Throughout the process of aging, dynamic changes of bone material, micro- and macro-architecture result in a loss of strength and therefore in an increased likelihood of fragility fractures. To date, precise contributions of age-related changes in bo...

Deep Learning for Automatic Calcium Scoring in CT: Validation Using Multiple Cardiac CT and Chest CT Protocols.

Radiology
Background Although several deep learning (DL) calcium scoring methods have achieved excellent performance for specific CT protocols, their performance in a range of CT examination types is unknown. Purpose To evaluate the performance of a DL method ...

Deep Learning in Physiological Signal Data: A Survey.

Sensors (Basel, Switzerland)
Deep Learning (DL), a successful promising approach for discriminative and generative tasks, has recently proved its high potential in 2D medical imaging analysis; however, physiological data in the form of 1D signals have yet to be beneficially expl...

DTranNER: biomedical named entity recognition with deep learning-based label-label transition model.

BMC bioinformatics
BACKGROUND: Biomedical named-entity recognition (BioNER) is widely modeled with conditional random fields (CRF) by regarding it as a sequence labeling problem. The CRF-based methods yield structured outputs of labels by imposing connectivity between ...

Short Keynote Paper: Mainstreaming Personalized Healthcare-Transforming Healthcare Through New Era of Artificial Intelligence.

IEEE journal of biomedical and health informatics
Medicine has entered the digital era, driven by data from new modalities, especially genomics and imaging, as well as new sources such as wearables and Internet of Things. As we gain a deeper understanding of the disease biology and how diseases affe...

Channel Attention Module With Multiscale Grid Average Pooling for Breast Cancer Segmentation in an Ultrasound Image.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Breast cancer accounts for the second-largest number of deaths in women around the world, and more than 8% of women will suffer from the disease in their lifetime. Mortality due to breast cancer can be reduced by its early and precise diagnosis. Many...

PANDA: Prioritization of autism-genes using network-based deep-learning approach.

Genetic epidemiology
Understanding the genetic background of complex diseases and disorders plays an essential role in the promising precision medicine. The evaluation of candidate genes, however, requires time-consuming and expensive experiments given a large number of ...

Assisted phase and step annotation for surgical videos.

International journal of computer assisted radiology and surgery
PURPOSE: Annotation of surgical videos is a time-consuming task which requires specific knowledge. In this paper, we present and evaluate a deep learning-based method that includes pre-annotation of the phases and steps in surgical videos and user as...

Learning Mobile Manipulation through Deep Reinforcement Learning.

Sensors (Basel, Switzerland)
Mobile manipulation has a broad range of applications in robotics. However, it is usually more challenging than fixed-base manipulation due to the complex coordination of a mobile base and a manipulator. Although recent works have demonstrated that d...

Deep learning improves the ability of sgRNA off-target propensity prediction.

BMC bioinformatics
BACKGROUND: CRISPR/Cas9 system, as the third-generation genome editing technology, has been widely applied in target gene repair and gene expression regulation. Selection of appropriate sgRNA can improve the on-target knockout efficacy of CRISPR/Cas9...