Information Processing in Medical Imaging

28th International Conference, IPMI 2023, San Carlos de Bariloche, Argentina, June 18-23, 2023, Proceedings
1. Auflage 2023
ISBN: 978-3-031-34047-5
Verlag: Springer Nature Switzerland

Buch, Englisch, Band 13939, 839 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 1282 g

Reihe: Lecture Notes in Computer Science

This book constitutes the proceedings of the 28th International Conference on Information Processing in Medical Imaging, IPMI 2023, which took place in San Carlos de Bariloche, Argentina, in June 2023.

The 63 full papers presented in this volume were carefully reviewed and selected from 169 submissions. They were organized in topical sections as follows: biomarkers; brain connectomics; computer-aided diagnosis/surgery; domain adaptation; geometric deep learning; groupwise atlasing; harmonization; federated learning; image synthesis; image enhancement; multimodal learning; registration; segmentation; self supervised learning; surface analysis and segmentation.

Zielgruppe

Research

Autoren/Hrsg.

Frangi, Alejandro

Navab, Nassir

de Bruijne, Marleen

Wassermann, Demian

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
• Mathematik | Informatik EDV | Informatik Informatik Mathematik für Informatiker
• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung
• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
• Mathematik | Informatik EDV | Informatik Angewandte Informatik

Inhaltsverzeichnis

Biomarkers Resolving quantitative MRI model degeneracy with machine learning via training data distribution design.- Subtype and stage inference with timescales.- Brain connectomics HoloBrain: A Harmonic Holography for Self-organized Brain Function.- Species-Shared and -Specific Brain Functional Connectomes Revealed by Shared-Unique Variational Autoencoder.- mSPD-NN: A Geometrically Aware Neural Framework for Biomarker Discovery from Functional Connectomics Manifolds.- Computer-Aided Diagnosis/Surgery Diffusion Model based Semi-supervised Learning on Brain Hemorrhage Images for Efficient Midline Shift Quantification.- Don’t PANIC: Prototypical Additive Neural Network for Interpretable Classification of Alzheimer’s Disease.- Filtered trajectory recovery: a continuous extension to event-based model for Alzheimer’s disease progression modeling.- Live image-based neurosurgical guidance and roadmap generation using unsupervised embedding.- Meta-information-aware Dual-path Transformer for Differential Diagnosis of Multi-type Pancreatic Lesions in Multi-phase CT.- MetaViT: Metabolism-Aware Vision Transformer for Differential Diagnosis of Parkinsonism with 18F-FDG PET.- Multi-task Multi-instance Learning for Jointly Diagnosis and Prognosis of Early-stage Breast Invasive Carcinoma from Whole-slide Pathological Images.- On Fairness of Medical Image Classification with Multiple Sensitive Attributes via Learning Orthogonal Representations.- Pixel-level explanation of multiple instance learning models in biomedical single cell images.- Marr Transient Hemodynamics Prediction Using an Efficient Octree-Based Deep Learning Model.- Weakly Semi-Supervised Detection in Lung Ultrasound Videos.- Optimization Differentiable Gamma Index-based loss functions: accelerating Monte-Carlo radiotherapy dose simulation.- Diversified stochastic orthonormal projective non-negative matrix factorization for big neuroimaging data.- Reconstruction Deep Physics-informed Super-resolution of Cardiac 4D-flow MRI.- Fast-MC-PET: A Novel Deep Learning-aid Motion Correction and Reconstruction Framework for Accelerated PET.- MeshDeform: Surface Reconstruction of Subcortical Structures via Human Brain MRI.- Neural Implicit k-Space for Binning-free Non-Cartesian Cardiac MR Imaging.