AIMC Topic: Machine Learning

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Label-activating framework for zero-shot learning.

Neural networks : the official journal of the International Neural Network Society
Existing zero-shot learning (ZSL) models usually learn mappings between visual space and semantic space. However, few of them take the label information into account. Indirect Attribute Prediction (IAP) learns the posterior probability of each attrib...

Using machine learning to explain the heterogeneity of schizophrenia. Realizing the promise and avoiding the hype.

Schizophrenia research
Despite extensive research and prodigious advances in neuroscience, our comprehension of the nature of schizophrenia remains rudimentary. Our failure to make progress is attributed to the extreme heterogeneity of this condition, enormous complexity o...

Nonconvulsive epileptic seizure monitoring with incremental learning.

Computers in biology and medicine
Nonconvulsive epileptic seizures (NCSz) and nonconvulsive status epilepticus (NCSE) are two neurological entities associated with increment in morbidity and mortality in critically ill patients. In a previous work, we introduced a method which accura...

A modality conversion approach to MV-DRs and KV-DRRs registration using information bottlenecked conditional generative adversarial network.

Medical physics
PURPOSE: As affordable equipment, electronic portal imaging devices (EPIDs) are wildly used in radiation therapy departments to verify patients' positions for accurate radiotherapy. However, these devices tend to produce visually ambiguous and low-co...

Physics-driven learning of x-ray skin dose distribution in interventional procedures.

Medical physics
PURPOSE: Radiation doses accumulated during very complicated image-guided x-ray procedures have the potential to cause stochastic, but also deterministic effects, such as skin rashes or even hair loss. To monitor and reduce radiation-related risks to...

CT Texture Analysis and Machine Learning Improve Post-ablation Prognostication in Patients with Adrenal Metastases: A Proof of Concept.

Cardiovascular and interventional radiology
INTRODUCTION: To assess the performance of pre-ablation computed tomography texture features of adrenal metastases to predict post-treatment local progression and survival in patients who underwent ablation using machine learning as a prediction tool...

A probabilistic method to estimate gait kinetics in the absence of ground reaction force measurements.

Journal of biomechanics
Human joint torques during gait are usually computed using inverse dynamics. This method requires a skeletal model, kinematics and measured ground reaction forces and moments (GRFM). Measuring GRFM is however only possible in a controlled environment...

Machine learning algorithms can classify outdoor terrain types during running using accelerometry data.

Gait & posture
BACKGROUND: Running is a popular physical activity that benefits health; however, running surface characteristics may influence loading impact and injury risk. Machine learning algorithms could automatically identify running surface from wearable mot...

Improvement of Epitope Prediction Using Peptide Sequence Descriptors and Machine Learning.

International journal of molecular sciences
In this work, we improved a previous model used for the prediction of proteomes as new B-cell epitopes in vaccine design. The predicted epitope activity of a queried peptide is based on its sequence, a known reference epitope sequence under specific ...