Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
Abstract Machine learning (ML) algorithms have made a tremendous impact in the field of
medical imaging. While medical imaging datasets have been growing in size, a challenge …
medical imaging. While medical imaging datasets have been growing in size, a challenge …
Feature selection: A data perspective
Feature selection, as a data preprocessing strategy, has been proven to be effective and
efficient in preparing data (especially high-dimensional data) for various data-mining and …
efficient in preparing data (especially high-dimensional data) for various data-mining and …
Challenges, evaluation and opportunities for open-world learning
Environmental changes can profoundly impact the performance of artificial intelligence
systems operating in the real world, with effects ranging from overt catastrophic failures to …
systems operating in the real world, with effects ranging from overt catastrophic failures to …
Machine learning and radiology
In this paper, we give a short introduction to machine learning and survey its applications in
radiology. We focused on six categories of applications in radiology: medical image …
radiology. We focused on six categories of applications in radiology: medical image …
Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease
Many machine learning and pattern classification methods have been applied to the
diagnosis of Alzheimer's disease (AD) and its prodromal stage, ie, mild cognitive impairment …
diagnosis of Alzheimer's disease (AD) and its prodromal stage, ie, mild cognitive impairment …
Transfer learning
Supervised machine learning techniques have already been widely studied and applied to
various real-world applications. However, most existing supervised algorithms work well …
various real-world applications. However, most existing supervised algorithms work well …
Multi-task feature learning via efficient l2, 1-norm minimization
The problem of joint feature selection across a group of related tasks has applications in
many areas including biomedical informatics and computer vision. We consider the l2, 1 …
many areas including biomedical informatics and computer vision. We consider the l2, 1 …
An empirical study of multifactorial PSO and multifactorial DE
Recently, the notion of Multifactorial Optimization (MFO) has emerged as a promising
approach for evolutionary multi-tasking by automatically exploiting the latent synergies …
approach for evolutionary multi-tasking by automatically exploiting the latent synergies …
Taxonomy of machine learning paradigms: A data‐centric perspective
Abstract Machine learning is a field composed of various pillars. Traditionally, supervised
learning (SL), unsupervised learning (UL), and reinforcement learning (RL) are the …
learning (SL), unsupervised learning (UL), and reinforcement learning (RL) are the …
Transfer learning for molecular cancer classification using deep neural networks
The emergence of deep learning has impacted numerous machine learning based
applications and research. The reason for its success lies in two main advantages: 1) it …
applications and research. The reason for its success lies in two main advantages: 1) it …