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Computer analysis of images and patterns : CAIP 2019 international workshops, ViMaBi and DL-UAV, Salerno, Italy, September 6, 2019 : proceedings

Author: Mario Vento; Gennaro Percannella; et al
Publisher: Cham : Springer, [2019] ©2019
Series: Communications in computer and information science, 1089.
Edition/Format:   eBook : Document : Conference publication : EnglishView all editions and formats
Summary:
This book constitutes the refereed proceedings of two workshops held at the 18th International Conference on Computer Analysis of Images and Patterns, CAIP 2019, held in Salerno, Italy, in September 2019: First Workshop on Deep-learning based Computer Vision for UAV, DL-UAV 2019, and the First Workshop on Visual Computing and Machine Learning for Biomedical Applications, ViMaBi 2019. The 12 papers presented in this  Read more...
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Genre/Form: Electronic books
Conference papers and proceedings
Congresses
Material Type: Conference publication, Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Mario Vento; Gennaro Percannella; et al
ISBN: 9783030299309 3030299309
OCLC Number: 1119391406
Description: 1 online resource : illustrations (some color)
Contents: Intro; Workshop Editors; Workshop Chairs; Preface; Organization; Contents; Workshop on Visual Computing and Machine Learning for Biomedical Applications (ViMaBi); Workshop on Visual Computing and Machine Learning for Biomedical Applications (ViMaBi); Workshop Description; Organization; Chairs; Program Committee; Additional Reviewers; Sponsor; Retinal Blood Vessels Segmentation: Improving State-of-the-Art Deep Methods; 1 Introduction; 2 Architectures; 2.1 Baseline: Birgui-Sekou Architecture; 2.2 First Proposal: Green Channel Inputs; 2.3 Second Proposal: U-Net Variation 2.4 Third Proposal: Poisson Loss Function3 Methodology; 3.1 Database; 3.2 Training; 3.3 Segmentation Mask Generation; 3.4 Performance Evaluation Protocol; 4 Results and Discussion; 5 Conclusion and Perspectives; References; A New Hybrid Method for Gland Segmentation in Histology Images; Abstract; 1 Introduction; 2 Related Work; 3 Method; 3.1 Image-Level Classification; 3.2 Pixel Level Classification; 4 Evaluation; 5 Results; 5.1 Results for Image-Level Classification; 5.2 Results for Pixel-Level Classification; 5.3 Methods Comparison; 6 Conclusion; References Residual Convolutional Neural Networks to Automatically Extract Significant Breast Density Features1 Introduction; 2 Data Collection; 3 Network Model; 3.1 Two Super-Classes Classification; 3.2 BI-RADS Classification; 4 Results; 5 Discussion and Conclusions; References; Combining Convolutional Neural Networks for Multi-context Microcalcification Detection in Mammograms; 1 Introduction; 2 The Proposed Approach; 2.1 The General Architecture; 2.2 Specializing the Architecture for C Detection; 2.3 Choosing the Training Parameters; 3 Experimental Results; 4 Conclusions and Future Work; References Classification of Autism Spectrum Disorder Through the Graph Fourier Transform of fMRI Temporal Signals Projected on Structural Connectome1 Introduction; 2 Materials and Methods; 2.1 Database; 2.2 Regions of Interest and Time-Series Extraction; 2.3 Graph Signal Processing and Graph Fourier Transform on Structural Graph; 2.4 Feature Extraction and Feature Selection; 2.5 Cross-Validation, Classification and Statistical Analysis; 2.6 Visualization of Cross-Validated Selected Features; 3 Results and Discussion; 4 Conclusion; References Radiomic and Dosiomic Profiling of Paediatric Medulloblastoma Tumours Treated with Intensity Modulated Radiation TherapyAbstract; 1 Introduction; 2 Study Population; 2.1 Clinical Data; 3 Imaging; 4 Dose Distribution Information; 5 Statistical Analysis and Machine Learning; 6 Conclusion; References; May Radiomic Data Predict Prostate Cancer Aggressiveness?; 1 Introduction; 2 Methods and Materials; 2.1 Patient Cohort; 2.2 Image Acquisition; 2.3 Image Segmentation; 2.4 Feature Extraction; 2.5 Feature Selection and Classification; 3 Results; 3.1 Radiomic Signatures Building
Series Title: Communications in computer and information science, 1089.
Other Titles: CAIP 2019
Responsibility: Mario Vento, Gennaro Percannella et al. (eds.).

Abstract:

This book constitutes the refereed proceedings of two workshops held at the 18th International Conference on Computer Analysis of Images and Patterns, CAIP 2019, held in Salerno, Italy, in September 2019: First Workshop on Deep-learning based Computer Vision for UAV, DL-UAV 2019, and the First Workshop on Visual Computing and Machine Learning for Biomedical Applications, ViMaBi 2019. The 12 papers presented in this volume were carefully reviewed and selected from 16 submissions and focus on all aspects of visual computing and machine learning for biomedical applications, and deep-learning based computer vision for UAV.

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