AIMC Topic: Machine Learning

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Machine learning models based on remote and proximal sensing as potential methods for in-season biomass yields prediction in commercial sorghum fields.

PloS one
Crop yield monitoring demonstrated the potential to improve agricultural productivity through improved crop breeding, farm management and commodity planning. Remote and proximal sensing offer the possibility to cut crop monitoring costs traditionally...

App-based symptom tracking to optimize SARS-CoV-2 testing strategy using machine learning.

PloS one
BACKGROUND: Tests are scarce resources, especially in low and middle-income countries, and the optimization of testing programs during a pandemic is critical for the effectiveness of the disease control. Hence, we aim to use the combination of sympto...

Real-Time 3D Facial Tracking via Cascaded Compositional Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
We propose to learn a cascade of globally-optimized modular boosted ferns (GoMBF) to solve multi-modal facial motion regression for real-time 3D facial tracking from a monocular RGB camera. GoMBF is a deep composition of multiple regression models wi...

Application of a Machine Learning Approach for the Analysis of Clinical and Radiomic Features of Pretreatment [F]-FDG PET/CT to Predict Prognosis of Patients with Endometrial Cancer.

Molecular imaging and biology
PURPOSE: To examine the prognostic significance of pretreatment 2-deoxy-2-[F]fluoro-D-glucose ([F]-FDG) positron emission tomography (PET)-based radiomic features using a machine learning approach in patients with endometrial cancers.

A clustering-based adaptive Neighborhood Retrieval Visualizer.

Neural networks : the official journal of the International Neural Network Society
We introduce a novel adaptive version of the Neighborhood Retrieval Visualizer (NeRV). We maintain the advantages of the conventional NeRV method, while proposing an improvement of the data samples' neighborhood width calculation, in the input and ou...

Machine Learning in Body Composition Analysis.

European urology focus
Body composition analysis (BCA) generates objective anthropometric data that can inform prognostication and treatment decisions across a wide variety of urologic conditions. A patient's body composition, specifically muscle and adipose tissue mass, m...

Machine learning research towards combating COVID-19: Virus detection, spread prevention, and medical assistance.

Journal of biomedical informatics
COVID-19 was first discovered in December 2019 and has continued to rapidly spread across countries worldwide infecting thousands and millions of people. The virus is deadly, and people who are suffering from prior illnesses or are older than the age...

A machine learning-based survival prediction model of high grade glioma by integration of clinical and dose-volume histogram parameters.

Cancer medicine
PURPOSE: Glioma is the most common type of primary brain tumor in adults, and it causes significant morbidity and mortality, especially in high-grade glioma (HGG) patients. The accurate prognostic prediction of HGG is vital and helpful for clinicians...

SIENA: Semi-automatic semantic enhancement of datasets using concept recognition.

Journal of biomedical semantics
BACKGROUND: The amount of available data, which can facilitate answering scientific research questions, is growing. However, the different formats of published data are expanding as well, creating a serious challenge when multiple datasets need to be...

Small Bowel Capsule Endoscopy and artificial intelligence: First or second reader?

Best practice & research. Clinical gastroenterology
Several machine learning algorithms have been developed in the past years with the aim to improve SBCE (Small Bowel Capsule Endoscopy) feasibility ensuring at the same time a high diagnostic accuracy. If past algorithms were affected by low performan...