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200718s2020 si o 101 0 eng d |
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|z 9811551987
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|a 10.1007/978-981-15-5199-4
|2 doi
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| 024 |
8 |
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|a 10.1007/978-981-15-5
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| 035 |
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|a (OCoLC)1175914111
|z (OCoLC)1164825852
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|2 bisacsh
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|a HCDD
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| 111 |
2 |
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|a International Conference on Medical Imaging and Computer-Aided Diagnosis
|d (2020 :
|c Oxford, England)
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| 245 |
1 |
0 |
|a Medical imaging and computer-aided diagnosis :
|b proceeding of 2020 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2020) /
|c Ruidan Su, Han Liu, editors.
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| 246 |
3 |
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|a MICAD 2020
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| 260 |
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|a Singapore :
|b Springer,
|c 2020.
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| 300 |
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|a 1 online resource (254 pages)
|
| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
1 |
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|a Lecture Notes in Electrical Engineering ;
|v v. 633
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| 500 |
|
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|a International conference proceedings.
|
| 588 |
0 |
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|a Print version record.
|
| 505 |
0 |
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|a Intro -- Preface -- Organization -- Honorable Chair -- General Chair -- Program Chairs -- Publication Chair -- Keynote Speakers -- Technical Program Committee -- Contents -- Computer Modeling and Laser Stereolithography in Cranio-Orbital Reconstructive Surgery -- 1 Introduction -- 2 Materials and Methods -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Sparse Representation Label Fusion Method Combining Pixel Grayscale Weight for Brain MR Segmentation -- 1 Introduction -- 2 Methods -- 2.1 Atlas Registration -- 2.2 Parse Representation Method -- 2.3 Pixel Grayscale Weight Setting
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| 505 |
8 |
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|a 2.4 Label Fusion -- 3 Experiments and Results -- 3.1 Segmentation Evaluation Index -- 3.2 Influence of the Iterations -- 3.3 Detailed Segmentation Results -- 4 Discussion and Conclusion -- References -- Deep Learning for Mental Illness Detection Using Brain SPECT Imaging -- 1 Introduction -- 2 Main Results: CNN Models for Single Conditions -- 2.1 CNN Model -- 2.2 Cross-validation with Few Samples -- 2.3 The Amber Zone -- 3 Conclusion and Future Work -- References -- Vessel Segmentation and Stenosis Quantification from Coronary X-Ray Angiograms -- 1 Introduction -- 2 Methodology
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| 505 |
8 |
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|a 2.1 Data Acquisition -- 2.2 Vessel Segmentation and Edge Detection -- 2.3 Quantitative Coronary Arteriography -- 3 Results -- 3.1 Contour Detection -- 3.2 Stenosis Quantification -- 4 Conclusions -- References -- Improved Brain Tumor Segmentation and Diagnosis Using an SVM-Based Classifier -- 1 Introduction -- 2 Background -- 3 Methodology -- 4 Results and Discussions -- 5 Conclusion and Future Scope -- References -- 3D-Reconstruction and Semantic Segmentation of Cystoscopic Images -- 1 Introduction -- 2 3D Reconstruction -- 2.1 Endoscope Calibration -- 2.2 Structure-from-Motion
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| 505 |
8 |
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|a 2.3 Current Work and Results -- 3 Deep Learning -- 3.1 Feed-Forward Neural Networks -- 3.2 RaVeNNA 4pi: Semantic Segmentation -- 4 Conclusion and Outlook -- References -- A Biomedical Survey on Osteoporosis Classification Techniques -- 1 Introduction -- 1.1 Motivation -- 2 Related Works -- 3 Medical Assessment of Osteoporosis -- 3.1 Background -- 4 Classification of Osteoporosis Diagnosis Techniques -- 4.1 Radiographic Techniques -- 4.2 Biochemistry Bio-Markers Classification -- 4.3 Invasive Techniques -- 4.4 Osteoporosis Biosensors Classification -- 5 Bone Turnover Markers (BTMs)
|
| 505 |
8 |
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|a 5.1 Advantages of Using BTMs -- 5.2 Disadvantage of Using BTMs -- 6 Proposed Simulations and Experimental Results -- 6.1 Bone Stress Properties in Osteoporosis -- 7 Conclusion and Future Works -- References -- Segment Medical Image Using U-Net Combining Recurrent Residuals and Attention -- 1 Introduction -- 2 Related Work -- 2.1 Deep Learning -- 2.2 Medical Segmentation Based on Deep Learning -- 2.3 Segmentation Research Based on U-Net -- 3 Method -- 3.1 U-Net Module -- 3.2 Recurrent Residuals Module -- 3.3 Attention Units -- 4 Experiment -- 4.1 Implementation Details -- 4.2 Evaluation Metric
|
| 500 |
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|a 4.3 Result
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| 500 |
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|a Includes author index.
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| 520 |
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|a This book covers virtually all aspects of image formation in medical imaging, including systems based on ionizing radiation (x-rays, gamma rays) and non-ionizing techniques (ultrasound, optical, thermal, magnetic resonance, and magnetic particle imaging) alike. In addition, it discusses the development and application of computer-aided detection and diagnosis (CAD) systems in medical imaging. Given its coverage, the book provides both a forum and valuable resource for researchers involved in image formation, experimental methods, image performance, segmentation, pattern recognition, feature extraction, classifier design, machine learning / deep learning, radiomics, CAD workstation design, human-computer interaction, databases, and performance evaluation.
|
| 650 |
|
0 |
|a Diagnostic imaging
|x Data processing
|v Congresses.
|
| 650 |
|
0 |
|a Diagnosis
|x Data processing
|v Congresses.
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| 650 |
|
7 |
|a Imaging systems & technology.
|2 bicssc
|
| 650 |
|
7 |
|a Image processing.
|2 bicssc
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| 650 |
|
7 |
|a Pattern recognition.
|2 bicssc
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| 650 |
|
7 |
|a Radiology.
|2 bicssc
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| 650 |
|
7 |
|a Biomedical engineering.
|2 bicssc
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| 650 |
|
7 |
|a Technology & Engineering
|x Electronics
|x General.
|2 bisacsh
|
| 650 |
|
7 |
|a Computers
|x Computer Graphics.
|2 bisacsh
|
| 650 |
|
7 |
|a Computers
|x Computer Vision & Pattern Recognition.
|2 bisacsh
|
| 650 |
|
7 |
|a Medical
|x Diagnostic Imaging.
|2 bisacsh
|
| 650 |
|
7 |
|a Technology & Engineering
|x Biomedical.
|2 bisacsh
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| 650 |
|
7 |
|a Diagnosis
|x Data processing
|2 fast
|
| 650 |
|
7 |
|a Diagnostic imaging
|x Data processing
|2 fast
|
| 655 |
|
7 |
|a proceedings (reports)
|2 aat
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| 655 |
|
7 |
|a Conference papers and proceedings
|2 fast
|
| 655 |
|
7 |
|a Conference papers and proceedings.
|2 lcgft
|
| 655 |
|
7 |
|a Actes de congrès.
|2 rvmgf
|
| 700 |
1 |
|
|a Su, Ruidan.
|
| 700 |
1 |
|
|a Liu, Han.
|
| 776 |
0 |
8 |
|i Print version:
|a Su, Ruidan.
|t Medical Imaging and Computer-Aided Diagnosis : Proceeding of 2020 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2020).
|d Singapore : Springer Singapore Pte. Limited, ©2020
|z 9789811551987
|
| 830 |
|
0 |
|a Lecture notes in electrical engineering ;
|v v. 633.
|
| 856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-981-15-5199-4
|y Click for online access
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| 903 |
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|a SPRING-ENGINE2020
|
| 994 |
|
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|a 92
|b HCD
|