AIMC Topic: Machine Learning

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End-to-End Active Object Tracking and Its Real-World Deployment via Reinforcement Learning.

IEEE transactions on pattern analysis and machine intelligence
We study active object tracking, where a tracker takes visual observations (i.e., frame sequences) as input and produces the corresponding camera control signals as output (e.g., move forward, turn left, etc.). Conventional methods tackle tracking an...

Predicting the cytotoxicity of chemicals using ensemble learning methods and molecular fingerprints.

Journal of applied toxicology : JAT
The prediction of compound cytotoxicity is an important part of the drug discovery process. However, it usually appears as poor predictive performance because the datasets are high-throughput and have a class-imbalance problem. In this study, several...

Generative Adversarial Networks for Facilitating Stain-Independent Supervised and Unsupervised Segmentation: A Study on Kidney Histology.

IEEE transactions on medical imaging
A major challenge in the field of segmentation in digital pathology is given by the high effort for manual data annotations in combination with many sources introducing variability in the image domain. This requires methods that are able to cope with...

Estimating Scale-Invariant Future in Continuous Time.

Neural computation
Natural learners must compute an estimate of future outcomes that follow from a stimulus in continuous time. Widely used reinforcement learning algorithms discretize continuous time and estimate either transition functions from one step to the next (...

Comprehensive and Empirical Evaluation of Machine Learning Algorithms for Small Molecule LC Retention Time Prediction.

Analytical chemistry
Liquid chromatography is a core component of almost all mass spectrometric analyses of (bio)molecules. Because of the high-throughput nature of mass spectrometric analyses, the interpretation of these chromatographic data increasingly relies on infor...

Machine Learning in Relation to Emergency Medicine Clinical and Operational Scenarios: An Overview.

The western journal of emergency medicine
Health informatics is a vital technology that holds great promise in the healthcare setting. We describe two prominent health informatics tools relevant to emergency care, as well as the historical background and the current state of informatics. We ...

A randomized controlled trial of suicide prevention training for primary care providers: a study protocol.

BMC medical education
BACKGROUND: Suicide is a national public health crisis and a critical patient safety issue. It is the 10th leading cause of death overall and the second leading cause of death among adolescents and young adults (15-34 years old). Research shows 80% o...

Random forest prediction of Alzheimer's disease using pairwise selection from time series data.

PloS one
Time-dependent data collected in studies of Alzheimer's disease usually has missing and irregularly sampled data points. For this reason time series methods which assume regular sampling cannot be applied directly to the data without a pre-processing...

High-Quality Immunohistochemical Stains Through Computational Assay Parameter Optimization.

IEEE transactions on bio-medical engineering
Accurate profiling of tumors using immunohistochemistry (IHC) is essential in cancer diagnosis. The inferences drawn from IHC-stained images depend to a great extent on the quality of immunostaining, which is in turn affected strongly by assay parame...