| Description |
1 online resource : illustrations |
| Series |
Intelligent systems reference library ; volume 140 |
|
Intelligent systems reference library ; v. 140.
|
| Contents |
Intro; Foreword; Preface; Acknowledgements; Contents; Advanced Machine Learning in Computer-Aided Systems; Multi-modality Feature Learning in Diagnoses of Alzheimer's Disease; 1 Introduction; 2 Subjects; 2.1 Data Acquisition; 2.2 Image Analysis; 3 Multi-task Feature Selection (MTFS); 3.1 Method; 3.2 Multimodal Data Fusion and Classification; 3.3 Validation; 3.4 Results; 4 Manifold Regularized Multi-task Feature Selection (M2TFS); 4.1 Manifold Regularized MTFS (M2TFS); 4.2 Classification; 4.3 Results; 5 Label-Aligned Multi-task Feature Selection (LAMTFS); 5.1 Method |
|
5.2 Experiments and Results6 Discriminative Multi-task Feature Selection (DMTFS); 6.1 Method; 6.2 Experimental Results; 7 Conclusion; References; A Comparative Study of Modern Machine Learning Approaches for Focal Lesion Detection and Classification in Medical Images: BoVW, CNN and MTANN; 1 Introduction; 2 Methods; 2.1 Massive-Training Artificial Neural Networks (MTANNs); 2.2 Convolutional Neural Networks (CNNs); 2.3 Bag of Visual Words with Fisher Encoding; 3 Datasets; 3.1 Database for Lung Nodule Detection; 3.2 Database for Colorectal Polyp Detection |
|
3.3 Database for Lung Nodule Classification4 Candidate Generation and Data Augmentation; 5 Experiments; 5.1 CNNs Versus Fisher Vectors; 5.2 CNNs Versus MTANNs; 6 Discussion; 7 Conclusion; References; 3 Introduction to Binary Coordinate Ascent: New Insights into Efficient Feature Subset Selection for Machine Learning; Abstract; 1 Introduction; 2 Methods; 2.1 Coordinate Descent Algorithm; 2.2 Binary Coordinate Ascent Algorithm; 2.3 BCA-Based Wrapper FS; 3 Experimental Results; 4 Discussion; 5 Conclusion; Acknowledgements; References; Computer-Aided Detection |
|
4 Automated Lung Nodule Detection Using Positron Emission Tomography/Computed TomographyAbstract; 1 Introduction; 1.1 Related Works; 1.2 Objectives; 2 Methods; 2.1 Method Overview; 2.2 Nodule Detection Using CT Images; 2.2.1 Lung Segmentation; 2.2.2 Nodule Enhancement and Segmentation; 2.3 Nodule Detection in PET Images; 2.3.1 SUV Transformation; 2.3.2 Detection of Initial Candidates; 2.3.3 Initial FP Reduction; 2.4 Integration and False Positive Reduction; 2.4.1 Calculation of Characteristic Features; 2.4.2 Rule-Based Classifier; 2.4.3 SVM Classifiers; 3 Experiments; 3.1 Materials |
|
3.2 Evaluation Methods3.3 Results; 4 Discussions; 5 Conclusion; Acknowledgements; References; Detecting Mammographic Masses via Image Retrieval and Discriminative Learning; 1 Introduction; 2 Related Work; 2.1 Learning-Based CAD Methods; 2.2 CBIR-Based CAD Methods; 3 Mass Detection via Retrieval and Learning; 3.1 Local Feature Voting-Based Mass Retrieval; 3.2 Learning Similarity Thresholds; 3.3 Detection of Masses; 4 Experiments; 4.1 Dataset; 4.2 Mass Detection Performance; 4.3 Mass Retrieval Performance; 5 Conclusions and Discussions; References; Computer-Aided Diagnosis |
| Summary |
This book offers the first comprehensive overview of artificial intelligence (AI) technologies in decision support systems for diagnosis based on medical images, presenting cutting-edge insights from thirteen leading research groups around the world. Medical imaging offers essential information on patients? medical condition, and clues to causes of their symptoms and diseases. Modern imaging modalities, however, also produce a large number of images that physicians have to accurately interpret. This can lead to an?information overload? for physicians, and can complicate their decision-making. As such, intelligent decision support systems have become a vital element in medical-image-based diagnosis and treatment. Presenting extensive information on this growing field of AI, the book offers a valuable reference guide for professors, students, researchers and professionals who want to learn about the most recent developments and advances in the field |
| Bibliography |
Includes bibliographical references |
| Notes |
Online resource; title from PDF title page (EBSCO, viewed January, 22, 2018) |
| Subject |
Diagnostic imaging -- Data processing
|
|
Artificial intelligence -- Medical applications.
|
|
Biomedical engineering.
|
|
Artificial intelligence.
|
|
Radiology.
|
|
HEALTH & FITNESS -- Diseases -- General.
|
|
MEDICAL -- Clinical Medicine.
|
|
MEDICAL -- Diseases.
|
|
MEDICAL -- Evidence-Based Medicine.
|
|
MEDICAL -- Internal Medicine.
|
|
Artificial intelligence -- Medical applications
|
|
Diagnostic imaging -- Data processing
|
| Form |
Electronic book
|
| Author |
Suzuki, Kenji, editor
|
|
Chen, Yisong, editor
|
| ISBN |
9783319688435 |
|
331968843X |
|
