Medical imaging and computer-aided diagnosis : proceeding of 2020 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2020) /
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| Meeting name: | International Conference on Medical Imaging and Computer-Aided Diagnosis (2020 : Oxford, England) |
|---|---|
| Imprint: | Singapore : Springer, 2020. |
| Description: | 1 online resource (254 p.). |
| Language: | English |
| Series: | Lecture Notes in Electrical Engineering ; v.633 Lecture notes in electrical engineering ; v. 633. |
| Subject: | |
| Format: | E-Resource Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/12606432 |
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| 111 | 2 | |a International Conference on Medical Imaging and Computer-Aided Diagnosis |d (2020 : |c Oxford, England) | |
| 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. |
| 246 | 3 | |a MICAD 2020 | |
| 260 | |a Singapore : |b Springer, |c 2020. | ||
| 300 | |a 1 online resource (254 p.). | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Lecture Notes in Electrical Engineering ; |v v.633 | |
| 500 | |a International conference proceedings. | ||
| 500 | |a Description based upon print version of record. | ||
| 505 | 0 | |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 | |
| 505 | 8 | |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 | |
| 505 | 8 | |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 | |
| 505 | 8 | |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 | |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 | |a 4.3 Result | ||
| 500 | |a Includes author index. | ||
| 520 | |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. | |
| 650 | 7 | |a Imaging systems & technology. |2 bicssc | |
| 650 | 7 | |a Image processing. |2 bicssc | |
| 650 | 7 | |a Pattern recognition. |2 bicssc | |
| 650 | 7 | |a Radiology. |2 bicssc | |
| 650 | 7 | |a Biomedical engineering. |2 bicssc | |
| 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 | |
| 650 | 7 | |a Diagnosis |x Data processing |2 fast |0 (OCoLC)fst00892279 | |
| 650 | 7 | |a Diagnostic imaging |x Data processing |2 fast |0 (OCoLC)fst00892357 | |
| 655 | 0 | |a Electronic books. | |
| 655 | 4 | |a Electronic books. | |
| 655 | 7 | |a Conference papers and proceedings |2 fast |0 (OCoLC)fst01423772 | |
| 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,c2020 |z 9789811551987 |
| 830 | 0 | |a Lecture notes in electrical engineering ; |v v. 633. |0 http://id.loc.gov/authorities/names/no2007099709 | |
| 903 | |a HeVa | ||
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| 928 | |t Library of Congress classification |a RC78.7.D53 |l Online |c UC-FullText |u https://link.springer.com/10.1007/978-981-15-5199-4 |z Springer Nature |g ebooks |i 12622040 | ||