Medical image computing and computer assisted intervention - MICCAI 2023 : 26th International Conference, Vancouver, BC, Canada, October 8-12, 2023, Proceedings. Part I

Medical image computing and computer assisted intervention - MICCAI 2023 : Part I / 26th International Conference, Vancouver, BC, Canada, October 8-12, 2023, Proceedings. Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor, editors.

The ten-volume set LNCS 14220, 14221, 14222, 14223, 14224, 14225, 14226, 14227, 14228, and 14229 constitutes the refereed proceedings of the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, which was held in Vancouver, Canada, in October 2023....

Full description

Saved in:
Bibliographic Details
Corporate Author: International Conference on Medical Image Computing and Computer-Assisted Intervention Vancouver, B.C. ; Online
Other Authors: Greenspan, Hayit (Editor), Madabhushi, Anant (Editor), Mousavi, Parvin (Editor), Salcudean, Septimiu Edmund (Editor), Duncan, James, 1951- (Editor), Syeda-Mahmood, Tanveer (Editor), Taylor, Russell (Editor)
Format: eBook
Language:English
Published: Cham : Springer, 2023.
Series:Lecture notes in computer science ; 14220.
Subjects:
Diagnostic imaging > Data processing > Congresses.
Diagnostic imaging > Data processing
proceedings (reports)
Conference papers and proceedings
Conference papers and proceedings.
Actes de congrès.
Online Access:Click for online access
Table of Contents:
  • Intro
  • Preface
  • Organization
  • Contents - Part I
  • Machine Learning with Limited Supervision
  • PET-Diffusion: Unsupervised PET Enhancement Based on the Latent Diffusion Model
  • 1 Introduction
  • 2 Method
  • 2.1 Image Compression
  • 2.2 Latent Diffusion Model
  • 2.3 Implementation Details
  • 3 Experiments
  • 3.1 Dataset
  • 3.2 Ablation Analysis
  • 3.3 Comparison with State-of-the-Art Methods
  • 3.4 Generalization Evaluation
  • 4 Conclusion and Limitations
  • References
  • MedIM: Boost Medical Image Representation via Radiology Report-Guided Masking
  • 1 Introduction
  • 2 Approach
  • 2.1 Image and Text Encoders
  • 2.2 Report-Guided Mask Generation
  • 2.3 Decoder for Reconstruction
  • 2.4 Objective Function
  • 2.5 Downstream Transfer Learning
  • 3 Experiments and Results
  • 3.1 Experimental Details
  • 3.2 Comparisons with Different Pre-training Methods
  • 3.3 Discussions
  • 4 Conclusion
  • References
  • UOD: Universal One-Shot Detection of Anatomical Landmarks
  • 1 Introduction
  • 2 Method
  • 2.1 Stage I: Contrastive Learning
  • 2.2 Stage II: Supervised Learning
  • 3 Experiment
  • 3.1 Experimental Results
  • 4 Conclusion
  • References
  • S2ME: Spatial-Spectral Mutual Teaching and Ensemble Learning for Scribble-Supervised Polyp Segmentation
  • 1 Introduction
  • 2 Methodology
  • 2.1 Preliminaries
  • 2.2 S2ME: Spatial-Spectral Mutual Teaching and Ensemble Learning
  • 3 Experiments
  • 3.1 Experimental Setup
  • 3.2 Results and Analysis
  • 3.3 Ablation Studies
  • 4 Conclusion
  • References
  • Modularity-Constrained Dynamic Representation Learning for Interpretable Brain Disorder Analysis with Functional MRI
  • 1 Introduction
  • 2 Materials and Methodology
  • 2.1 Subjects and Image Preprocessing
  • 2.2 Proposed Method
  • 3 Experiment
  • 4 Discussion
  • 5 Conclusion and Future Work
  • References
  • Anatomy-Driven Pathology Detection on Chest X-rays
  • 1 Introduction
  • 2 Related Work
  • 3 Method
  • 3.1 Model
  • 3.2 Inference
  • 3.3 Training
  • 3.4 Dataset
  • 4 Experiments and Results
  • 4.1 Experimental Setup and Baselines
  • 4.2 Pathology Detection Results
  • 5 Discussion and Conclusion
  • References
  • VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel Synthesis
  • 1 Introduction
  • 2 Methods
  • 3 Experimental Setup
  • 4 Results
  • 5 Conclusions
  • References
  • Dense Transformer based Enhanced Coding Network for Unsupervised Metal Artifact Reduction
  • 1 Introduction
  • 2 Methodology
  • 2.1 Hierarchical Disentangling Encoder (HDE)
  • 2.2 Dense Transformer for Disentanglement (DTD)
  • 2.3 Second-Order Disentanglement for MA Reduction (SOD-MAR)
  • 2.4 Loss Function
  • 3 Empirical Results
  • 3.1 Ablation Study
  • 3.2 Comparison to State-of-the-Art (SOTA)
  • 4 Conclusion
  • References
  • Multi-scale Cross-restoration Framework for Electrocardiogram Anomaly Detection
  • 1 Introduction
  • 2 Method
  • 2.1 Multi-scale Cross-restoration

Similar Items