AIMC Topic: Machine Learning

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Combining deep learning and coherent anti-Stokes Raman scattering imaging for automated differential diagnosis of lung cancer.

Journal of biomedical optics
Lung cancer is the most prevalent type of cancer and the leading cause of cancer-related deaths worldwide. Coherent anti-Stokes Raman scattering (CARS) is capable of providing cellular-level images and resolving pathologically related features on hum...

Impact of Clinical Parameters in the Intrahost Evolution of HIV-1 Subtype B in Pediatric Patients: A Machine Learning Approach.

Genome biology and evolution
Determining the factors modulating the genetic diversity of HIV-1 populations is essential to understand viral evolution. This study analyzes the relative importance of clinical factors in the intrahost HIV-1 subtype B (HIV-1B) evolution and in the f...

Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large-scale genetic studies.

GigaScience
Genetic analyses of plant root systems require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a...

Deep machine learning provides state-of-the-art performance in image-based plant phenotyping.

GigaScience
In plant phenotyping, it has become important to be able to measure many features on large image sets in order to aid genetic discovery. The size of the datasets, now often captured robotically, often precludes manual inspection, hence the motivation...

Machine Learning Approaches in Cardiovascular Imaging.

Circulation. Cardiovascular imaging
Cardiovascular imaging technologies continue to increase in their capacity to capture and store large quantities of data. Modern computational methods, developed in the field of machine learning, offer new approaches to leveraging the growing volume ...

Machine Learning in Health Care.

Journal of perianesthesia nursing : official journal of the American Society of PeriAnesthesia Nurses

Trajectory Predictor by Using Recurrent Neural Networks in Visual Tracking.

IEEE transactions on cybernetics
Motion models have been proved to be a crucial part in the visual tracking process. In recent trackers, particle filter and sliding windows-based motion models have been widely used. Treating motion models as a sequence prediction problem, we can est...

Distributed Intrinsic Functional Connectivity Patterns Predict Diagnostic Status in Large Autism Cohort.

Brain connectivity
Diagnosis of autism spectrum disorder (ASD) currently relies on behavioral observations because brain markers are unknown. Machine learning approaches can identify patterns in imaging data that predict diagnostic status, but most studies using functi...

Identifying Distinct Subgroups of ICU Patients: A Machine Learning Approach.

Critical care medicine
OBJECTIVES: Identifying subgroups of ICU patients with similar clinical needs and trajectories may provide a framework for more efficient ICU care through the design of care platforms tailored around patients' shared needs. However, objective methods...