AIMC Topic: Supervised Machine Learning

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Discriminative Mixture Variational Autoencoder for Semisupervised Classification.

IEEE transactions on cybernetics
In this article, a deep probability model, called the discriminative mixture variational autoencoder (DMVAE), is developed for the feature extraction in semisupervised learning. The DMVAE consists of three parts: 1) the encoding; 2) decoding; and 3) ...

Semisupervised Multiple Choice Learning for Ensemble Classification.

IEEE transactions on cybernetics
Ensemble learning has many successful applications because of its effectiveness in boosting the predictive performance of classification models. In this article, we propose a semisupervised multiple choice learning (SemiMCL) approach to jointly train...

Learning From Weakly Labeled Data Based on Manifold Regularized Sparse Model.

IEEE transactions on cybernetics
In multilabel learning, each training example is represented by a single instance, which is relevant to multiple class labels simultaneously. Generally, all relevant labels are considered to be available for labeled data. However, instances with a fu...

Supervised Machine Learning-Based Decision Support for Signal Validation Classification.

Drug safety
INTRODUCTION: Signal validation in pharmacovigilance is the process of evaluating data to decide whether evidence is sufficient to justify further assessment of a detected signal. During the signal validation process, safety experts in our organizati...

WVALE: Weak variational autoencoder for localisation and enhancement of COVID-19 lung infections.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The COVID-19 pandemic is a major global health crisis of this century. The use of neural networks with CT imaging can potentially improve clinicians' efficiency in diagnosis. Previous studies in this field have primarily foc...

Exploration of Black Boxes of Supervised Machine Learning Models: A Demonstration on Development of Predictive Heart Risk Score.

Computational intelligence and neuroscience
Machine learning (ML) often provides applicable high-performance models to facilitate decision-makers in various fields. However, this high performance is achieved at the expense of the interpretability of these models, which has been criticized by p...

MultiHeadGAN: A deep learning method for low contrast retinal pigment epithelium cell segmentation with fluorescent flatmount microscopy images.

Computers in biology and medicine
BACKGROUND: Retinal pigment epithelium (RPE) aging is an important cause of vision loss. As RPE aging is accompanied by changes in cell morphological features, an accurate segmentation of RPE cells is a prerequisite to such morphology analyses. Due t...

What Actually Works for Activity Recognition in Scenarios with Significant Domain Shift: Lessons Learned from the 2019 and 2020 Sussex-Huawei Challenges.

Sensors (Basel, Switzerland)
From 2018 to 2021, the Sussex-Huawei Locomotion-Transportation Recognition Challenge presented different scenarios in which participants were tasked with recognizing eight different modes of locomotion and transportation using sensor data from smartp...

Centroid Estimation With Guaranteed Efficiency: A General Framework for Weakly Supervised Learning.

IEEE transactions on pattern analysis and machine intelligence
In this paper, we propose a general framework termed centroid estimation with guaranteed efficiency (CEGE) for weakly supervised learning (WSL) with incomplete, inexact, and inaccurate supervision. The core of our framework is to devise an unbiased a...

Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology.

Medical image analysis
Artificial intelligence (AI) can extract visual information from histopathological slides and yield biological insight and clinical biomarkers. Whole slide images are cut into thousands of tiles and classification problems are often weakly-supervised...