Heterogeneous data analysis: Online learning for medical-image-based diagnosis

Motai, Yuichi; Siddique, Nahian; Yoshida, Hiroyuki

doi:10.1016/j.patcog.2016.09.035

Cited by 15 publications

(5 citation statements)

References 43 publications

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“…This idea provides a set of simple, fast and efficient techniques regarding computation and memory as well. Recent successful approaches based on this technique have been used to medical diagnosis [1], detecting topics on text streams [2], action recognition [3] or face recognition [4] among others.…”

Section: Introductionmentioning

confidence: 99%

Passive-Aggressive online learning with nonlinear embeddings

Jorge

Paredes

2018

Pattern Recognition

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Nowadays, there is an increasing demand for machine learning techniques which can deal with problems where the instances are produced as a stream or in real time. In these scenarios, online learning is able to learn a model from data that comes continuously. The adaptability, efficiency and scalability of online learning techniques have been gaining interest last years with the increasing amount of data generated every day. In this paper, we propose a novel binary classification approach based on nonlinear mapping functions under an online learning framework. The non-convex optimization problem that arises is split into three different convex problems that are solved by means of Passive-Aggressive Online Learning. We evaluate both the adaptability and generalization of our model through several experiments comparing with the state of the art techniques. We improve significantly the results in several datasets widely used previously by the online learning community.

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Section: Introductionmentioning

confidence: 99%

Passive-Aggressive online learning with nonlinear embeddings

Jorge

Paredes

2018

Pattern Recognition

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“…Their features are often heterogeneous and consist of numerical and non-numerical features with different properties [142]- [145]. For example, in clinical research [146] medical data are collected from different sources, such as demographics, disease history, medication, allergies, biomarkers, medical images, or genetic markers, each of which offers a different partial view of a patientąŕs condition [147]. As a result, it is difficult to evaluate heterogeneous features concurrently.…”

Section: ) Heterogeneous Datamentioning

confidence: 99%

Feature Selection and Its Use in Big Data: Challenges, Methods, and Trends

2019

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Feature selection has been an important research area in data mining, which chooses a subset of relevant features for use in the model building. This paper aims to provide an overview of feature selection methods for big data mining. First, it discusses the current challenges and difficulties faced when mining valuable information from big data. A comprehensive review of existing feature selection methods in big data is then presented. Herein, we approach the review from two aspects: methods specific to a particular kind of big data with certain characteristics and applications of methods in classification analysis, which are significantly different to the existing review work. This paper also highlights the current issues of feature selection in big data and suggests the future research directions. INDEX TERMS Feature selection, big data, data mining, applications.

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“…This method is accurate and robust against outliers and image artefacts. Another benefit of this method is that it enables the easy alignment of clinical images obtained from multiple sources [81,82].…”

Section: Essential Components and Types Of Caos Systemsmentioning

confidence: 99%

Recent Trends, Technical Concepts and Components of Computer-Assisted Orthopedic Surgery Systems: A Comprehensive Review

Kubíček

Tomanec

Černý

et al. 2019

Sensors

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Computer-assisted orthopedic surgery (CAOS) systems have become one of the most important and challenging types of system in clinical orthopedics, as they enable precise treatment of musculoskeletal diseases, employing modern clinical navigation systems and surgical tools. This paper brings a comprehensive review of recent trends and possibilities of CAOS systems. There are three types of the surgical planning systems, including: systems based on the volumetric images (computer tomography (CT), magnetic resonance imaging (MRI) or ultrasound images), further systems utilize either 2D or 3D fluoroscopic images, and the last one utilizes the kinetic information about the joints and morphological information about the target bones. This complex review is focused on three fundamental aspects of CAOS systems: their essential components, types of CAOS systems, and mechanical tools used in CAOS systems. In this review, we also outline the possibilities for using ultrasound computer-assisted orthopedic surgery (UCAOS) systems as an alternative to conventionally used CAOS systems.

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Heterogeneous data analysis: Online learning for medical-image-based diagnosis

Cited by 15 publications

References 43 publications

Passive-Aggressive online learning with nonlinear embeddings

Passive-Aggressive online learning with nonlinear embeddings

Feature Selection and Its Use in Big Data: Challenges, Methods, and Trends

Recent Trends, Technical Concepts and Components of Computer-Assisted Orthopedic Surgery Systems: A Comprehensive Review

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