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Advances in Visual Computing

18th International Symposium, ISVC 2023, Lake Tahoe, NV, USA, October 16-18, 2023, Proceedings

"Advances in Visual Computing" is published by Springer in 2023 - Cham, it has 1 pages and the language of the book is English.


“Advances in Visual Computing” Metadata:

  • Title: Advances in Visual Computing
  • Authors:
  • Language: English
  • Number of Pages: 1
  • Publisher: Springer
  • Publish Date:
  • Publish Location: Cham

“Advances in Visual Computing” Subjects and Themes:

Edition Specifications:

  • Weight: 0.991
  • Pagination: xlii, 614

Edition Identifiers:

AI-generated Review of “Advances in Visual Computing”:


"Advances in Visual Computing" Description:

Open Data:

Intro -- Preface -- Organization -- Keynote Talks -- Machine Learning for Scientific Data Analysis and Visualization -- Estimating the Structure and Motion of Biomolecules at Atomic Resolutions -- Curriculum Learning and Active Learning, for Visual Object Recognition when Data is Scarce -- Have We Solved Image Correspondences? -- Visual Content Manipulation by Learning Generative Models -- Lights, Camera, Animation! Adaptive Simulation Methods for Training and Entertainment -- Beyond the Specs: A Computational and Human-Centered Approach to Wearability in AR/VR -- Contents - Part I -- Contents - Part II -- ST: Biomedical Image Analysis Techniques for Cancer Detection, Diagnosis and Management -- Hybrid Region and Pixel-Level Adaptive Loss for Mass Segmentation on Whole Mammography Images -- 1 Introduction -- 2 Related Work -- 2.1 Mass Segmentation on Whole Mammograms -- 2.2 Loss for Medical Image Segmentation -- 3 Methodology -- 3.1 Hybrid Pixel-Level Loss -- 3.2 Hybrid Region-Level Loss -- 3.3 Density-Adaptive Sample-Level Prioritizing Loss -- 4 Experimental Results -- 4.1 Datasets -- 4.2 Evaluation Metrics -- 4.3 Comparison with State-of-the-Art Methods -- 5 Conclusion -- References -- Deep Learning Based GABA Edited-MRS Signal Reconstruction -- 1 Introduction -- 2 Methods -- 2.1 Dataset -- 2.2 J-Difference Spectrum -- 2.3 Dual Branch Self-Attention Neural Network -- 2.4 Evaluation Metrics -- 3 Results and Discussion -- 4 Conclusion -- References -- Investigating the Impact of Attention on Mammogram Classification -- 1 Introduction -- 2 Data and Methods -- 2.1 Data Selection and Preprocessing -- 2.2 Selection of Models -- 2.3 Selection of Attention Methods -- 2.4 Training and Testing Process -- 3 Results and Discussion -- 3.1 Impact of Attention on CNN Performance -- 3.2 Impact of Model Architecture on Performance Differences

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