Clinical image-based procedures, fairness of AI in medical imaging, and ethical and philosophical issues in medical imaging : 12th International Workshop, CLIP 2023 1st International Workshop, FAIMI 2023 and 2nd International Workshop, EPIMI 2023 Vancouver, BC, Canada, October 8 and October 12, 2023 Proceedings / Stefan Wesarg, Esther Puyol Antón, John S. H. Baxter, Marius Erdt, Klaus Drechsler, Cristina Oyarzun Laura, Moti Freiman, Yufei Chen, Islem Rekik, Roy Eagleson, Aasa Feragen, Andrew P. King, Veronika Cheplygina, Melani Ganz-Benjaminsen, Enzo Ferrante, Ben Glocker, Daniel Moyer, Eikel Petersen, editors.

This book constitutes the refereed proceedings of the 12th International Workshop on Clinical Image-Based Procedures, CLIP 2023, the First MICCAI Workshop on Fairness of AI in Medical Imaging, FAIMI 2023, held in conjunction with MICCAI 2023, in October 2023, and the Second MICCAI Workshop on the Et...

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Bibliographic Details
Corporate Authors: CLIP (Workshop) Vancouver, B.C.), FAIMI (Workshop), EPIMI (Workshop), International Conference on Medical Image Computing and Computer-Assisted Intervention
Other Authors: Wesarg, Stefan (Editor), Puyol Anton, Esther (Editor), Baxter, John S. H. (Editor), Erdt, Marius (Editor), Drechsler, Klaus (Editor), Oyarzun Laura, Cristina (Editor), Freiman, Moti (Editor), Chen, Yufei (Editor), Rekik, Islem (Editor), Eagleson, Roy (Editor), Feragen, Aasa (Editor), King, Andrew P. (Andrew Peter) (Editor), Cheplygina, Veronika (Editor), Ganz-Benjaminsen, Melani (Editor), Ferrante, Enzo (Editor), Glocker, Ben (Editor), Moyer, Daniel (Editor), Petersen, Eikel (Editor)
Format: eBook
Language:English
Published: Cham, Switzerland : Springer, 2023.
Series:Lecture notes in computer science ; 14242.
Subjects:
Online Access:Click for online access
Table of Contents:
  • Intro
  • Additional Editors
  • CLIP Preface
  • CLIP Organization
  • FAIMI Preface
  • FAIMI Organization
  • EPIMI Preface
  • EPIMI Organization
  • Contents
  • CLIP
  • Automated Hand Joint Classification of Psoriatic Arthritis Patients Using Routinely Acquired Near Infrared Fluorescence Optical Imaging
  • 1 Introduction
  • 2 Background
  • 3 Method
  • 4 Results
  • 5 Discussion and Future Work
  • References
  • Automatic Neurocranial Landmarks Detection from Visible Facial Landmarks Leveraging 3D Head Priors
  • 1 Introduction
  • 2 Methods
  • 2.1 Datasets and Preprocessing
  • 2.2 Models Training and Evaluation
  • 3 Experimental Results
  • 3.1 Neurocranial Landmark Coordinates Prediction
  • 3.2 3DMM Validation
  • 3.3 Ablation Study
  • 4 Discussion and Conclusions
  • References
  • Subject-Specific Modelling of Knee Joint Motion for Routine Pre-operative Planning
  • 1 Introduction
  • 2 Method
  • 2.1 Contact Surface Model of PF and TF Joint
  • 2.2 Computation of Knee Flexion Angle
  • 2.3 Matching Tibia and Patella Poses
  • 3 Experiments and Discussions
  • 3.1 Evaluation of Generated Patella and Tibia Poses
  • 3.2 Evaluation of Tibia and Patella Pose Matching
  • 4 Conclusion
  • References
  • Towards Fine-Grained Polyp Segmentation and Classification
  • 1 Introduction
  • 2 Method
  • 2.1 Swin Transformer Encoder
  • 2.2 Multi-Scale Feature Enhancement
  • 2.3 Patch-Expanding Decoder
  • 2.4 Upsample Head
  • 2.5 Loss Function
  • 3 PolypSegm-ASH Dataset
  • 4 Results
  • 4.1 Experiments on PolypSegm-ASH
  • 4.2 Experiments on Binary Polyp Segmentation
  • 4.3 Ablation Study. Effect of Up-Samples Before Predictions
  • 5 Conclusion
  • References
  • Automated Orientation and Registration of Cone-Beam Computed Tomography Scans
  • 1 Introduction
  • 2 Materials
  • 3 Proposed Method
  • 3.1 Automated Standardized Orientation (ASO)
  • 3.2 Automated Registration (AReg)
  • 3.3 Evaluation Metrics
  • 3.4 Implementation
  • 4 Results
  • 4.1 Orientation
  • 4.2 Registration
  • 5 Discussion
  • 6 Conclusion
  • A Appendix
  • References
  • Deep Learning-Based Fast MRI Reconstruction: Improving Generalization for Clinical Translation
  • 1 Introduction
  • 2 Methods
  • 2.1 Background
  • 2.2 Physically-Primed DNN for MRI Reconstruction
  • 3 Experiments
  • 3.1 Dataset
  • 3.2 Experimental Methodology
  • 3.3 Results
  • 4 Conclusions
  • References
  • Uncertainty Based Border-Aware Segmentation Network for Deep Caries
  • 1 Introduction
  • 2 Related Work
  • 2.1 Dental Caries Image Segmentation
  • 2.2 Uncertainty Quantification
  • 3 Method
  • 3.1 Border-Aware Network Using SDF
  • 3.2 Uncertainty Based Caries Segmentation
  • 4 Experiments and Discussion
  • 4.1 Dataset and Settings
  • 4.2 Verification of SDF Effectiveness
  • 4.3 Verification of Model Robustness
  • 5 Conclusion
  • References
  • An Efficient and Accurate Neural Network Tool for Finding Correlation Between Gene Expression and Histological Images
  • 1 Introduction
  • 2 Methodology