Artificial Intelligence in Decision Support Systems for Diagnosis in Medical ImagingKenji Suzuki, Yisong Chen Springer, 9 janv. 2018 - 387 pages 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. |
Table des matières
| 3 | |
BoVW CNN and MTANN | 31 |
New Insights into Efficient Feature Subset Selection for Machine Learning | 59 |
ComputerAided Detection | 84 |
4 Automated Lung Nodule Detection Using Positron Emission TomographyComputed Tomography | 85 |
Detecting Mammographic Masses via Image Retrieval and Discriminative Learning | 111 |
ComputerAided Diagnosis | 133 |
HighOrder Statistics of MicroTexton for HEp2 Staining Pattern Classification | 135 |
8 Categorization of Lung Tumors into BenignMalignant SolidGGO and Typical BenignOthers | 192 |
9 Fuzzy Object Growth Model for Neonatal Brain MR Understanding | 209 |
ComputerAided Prognosis | 223 |
Accurate Prediction of Patients with Neurologic and Psychiatric Diseases via Multimodal MRI Analysis | 225 |
11 Radiomics in Medical ImagingDetection Extraction and Segmentation | 266 |
ComputerAided Therapy and Surgery | 334 |
Markerless Tumor Gating and Tracking for Lung Cancer Radiotherapy based on Machine Learning Techniques | 335 |
13 Image Guided and Robot Assisted Precision Surgery | 361 |
7 Intelligent Diagnosis of Breast Cancer Based on Quantitative BMode and Elastography Features | 165 |
Autres éditions - Tout afficher
Artificial Intelligence in Decision Support Systems for Diagnosis in Medical ... Kenji Suzuki,Yisong Chen Aucun aperçu disponible - 2019 |
Artificial Intelligence in Decision Support Systems for Diagnosis in Medical ... Kenji Suzuki,Yisong Chen Aucun aperçu disponible - 2018 |
Expressions et termes fréquents
accuracy algorithm alzheimer’s disease approach automated B-mode benign BI-RADS binary brain CAD system classification clinical cluster CNNs Computer computer-aided detection computer-aided diagnosis convolutional CT images database dataset descriptor dimensionality discriminative disease elastography evaluation false positives feature extraction feature selection feature subset filter Fisher vectors function gene gray level gray-level growth index HEp-2 cell heterogeneity IEEE Trans image features image guidance intensity kernel layer lesion lung cancer lung nodules lung tumors machine learning malignant mammograms masses matrix MCI-NC medical images modalities MTANNs multi-modality neural network neuroimaging nodule detection optimization parameters patients patterns performance PET images PET/CT pixel prediction proposed method quantitative radiologists radiomics regression retrieval ROC curve samples SFFS shown in Fig spatial statistics support vector machine surgery surgical robots Suzuki techniques texture features tissue tumor region visual voxel Zhang