AIMC Topic: Deep Learning

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ResMiCo: Increasing the quality of metagenome-assembled genomes with deep learning.

PLoS computational biology
The number of published metagenome assemblies is rapidly growing due to advances in sequencing technologies. However, sequencing errors, variable coverage, repetitive genomic regions, and other factors can produce misassemblies, which are challenging...

The three-dimensional weakly supervised deep learning algorithm for traumatic splenic injury detection and sequential localization: an experimental study.

International journal of surgery (London, England)
BACKGROUND: Splenic injury is the most common solid visceral injury in blunt abdominal trauma, and high-resolution abdominal computed tomography (CT) can adequately detect the injury. However, these lethal injuries sometimes have been overlooked in c...

Wide and deep learning based approaches for classification of Alzheimer's disease using genome-wide association studies.

PloS one
The increasing incidence of Alzheimer's disease (AD) has been leading towards a significant growth in socioeconomic challenges. A reliable prediction of AD might be useful to mitigate or at-least slow down its progression for which, identification of...

Estimating individual minimum calibration for deep-learning with predictive performance recovery: An example case of gait surface classification from wearable sensor gait data.

Journal of biomechanics
Clinical datasets often comprise multiple data points or trials sampled from a single participant. When these datasets are used to train machine learning models, the method used to extract train and test sets must be carefully chosen. Using the stand...

Impact of imperfection in medical imaging data on deep learning-based segmentation performance: An experimental study using synthesized data.

Medical physics
BACKGROUND: Clinical data used to train deep learning models are often not clean data. They can contain imperfections in both the imaging data and the corresponding segmentations.

Deep learning techniques in liver tumour diagnosis using CT and MR imaging - A systematic review.

Artificial intelligence in medicine
Deep learning has become a thriving force in the computer aided diagnosis of liver cancer, as it solves extremely complicated challenges with high accuracy over time and facilitates medical experts in their diagnostic and treatment procedures. This p...

Deep learning detection of melanoma metastases in lymph nodes.

European journal of cancer (Oxford, England : 1990)
BACKGROUND: In melanoma patients, surgical excision of the first draining lymph node, the sentinel lymph node (SLN), is a routine procedure to evaluate lymphogenic metastases. Metastasis detection by histopathological analysis assesses multiple tissu...

Fast screening and identification of illegal adulteration in dietary supplements and herbal medicines using molecular networking with deep-learning-based similarity algorithms.

Analytical and bioanalytical chemistry
Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is a powerful analytical tool used for adulteration inspection. Nevertheless, it is a challenging task to identify illegal adulterants that are not included in the library or are unexp...

Deep Learning vs Traditional Models for Predicting Hospital Readmission among Patients with Diabetes.

AMIA ... Annual Symposium proceedings. AMIA Symposium
A hospital readmission risk prediction tool for patients with diabetes based on electronic health record (EHR) data is needed. The optimal modeling approach, however, is unclear. In 2,836,569 encounters of 36,641 diabetes patients, deep learning (DL)...

Enabling Scientific Reproducibility through FAIR Data Management: An ontology-driven deep learning approach in the NeuroBridge Project.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Scientific reproducibility that effectively leverages existing study data is critical to the advancement of research in many disciplines including neuroscience, which uses imaging and electrophysiology modalities as primary endpoints or key dependenc...