AIMC Topic: Supervised Machine Learning

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Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation.

IEEE transactions on neural networks and learning systems
A common shortfall of supervised deep learning for medical imaging is the lack of labeled data, which is often expensive and time consuming to collect. This article presents a new semisupervised method for medical image segmentation, where the networ...

Mutual Information-Based Disentangled Neural Networks for Classifying Unseen Categories in Different Domains: Application to Fetal Ultrasound Imaging.

IEEE transactions on medical imaging
Deep neural networks exhibit limited generalizability across images with different entangled domain features and categorical features. Learning generalizable features that can form universal categorical decision boundaries across domains is an intere...

Improving prediction for medical institution with limited patient data: Leveraging hospital-specific data based on multicenter collaborative research network.

Artificial intelligence in medicine
BACKGROUND AND OBJECTIVE: Clinical decision support assisted by prediction models usually faces the challenges of limited clinical data and a lack of labels when the model is developed with data from a single medical institution. Accordingly, researc...

Development and Internal Validation of Supervised Machine Learning Algorithms for Predicting Clinically Significant Functional Improvement in a Mixed Population of Primary Hip Arthroscopy.

Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association
PURPOSE: To (1) develop and validate a machine learning algorithm to predict clinically significant functional improvements after hip arthroscopy for femoroacetabular impingement syndrome and to (2) develop a digital application capable of providing ...

Construction and Supervised Learning of Long-Term Grey Cognitive Networks.

IEEE transactions on cybernetics
Modeling a real-world system by means of a neural model involves numerous challenges that range from formulating transparent knowledge representations to obtaining reliable simulation errors. However, that knowledge is often difficult to formalize in...

Source identification of infectious diseases in networks via label ranking.

PloS one
BACKGROUND: Outbreaks of infectious diseases would cause great losses to the human society. Source identification in networks has drawn considerable interest in order to understand and control the infectious disease propagation processes. Unsatisfact...

Gut microbiome-based supervised machine learning for clinical diagnosis of inflammatory bowel diseases.

American journal of physiology. Gastrointestinal and liver physiology
Despite the availability of various diagnostic tests for inflammatory bowel diseases (IBD), misdiagnosis of IBD occurs frequently, and thus, there is a clinical need to further improve the diagnosis of IBD. As gut dysbiosis is reported in patients wi...

Optimal tuning of weighted kNN- and diffusion-based methods for denoising single cell genomics data.

PLoS computational biology
The analysis of single-cell genomics data presents several statistical challenges, and extensive efforts have been made to produce methods for the analysis of this data that impute missing values, address sampling issues and quantify and correct for ...