Del 15830 i serien Lecture Notes in Computer Science

Information Processing in Medical Imaging

29th International Conference, IPMI 2025, Kos, Greece, May 25–30, 2025, Proceedings, Part II

Häftad, Engelska, 2025

AvIpek Oguz,Shaoting Zhang,Dimitris N. Metaxas

1 079 kr

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This two-volume set LNCS 15829-15830 constitutes the proceedings of the 29th International Conference on Information Processing in Medical Imaging, IPMI 2025, held on Kos, Greece, during May 25-30, 2025.The 51 full papers presented in this volume were carefully reviewed and selected from 143 submissions. They were organized in topical sections as follows: Part I: Classification/Detection; Registration; Reconstruction; Image synthesis; Image enhancement; and Segmentation.Part II: Computer-aided diagnosis/surgery; Brain; Diffusion models; Self-supervised learning; Vision-language models; Shape analysis; and Time-series image analysis.

Produktinformation

Utgivningsdatum2025-08-07
Mått155 x 235 x 24 mm
Vikt645 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor408
FörlagSpringer International Publishing AG
ISBN9783031966248
OriginaltitelInformation Processing in Medical Imaging

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Computer-aided diagnosis/surgery: Concepts from Neurons: Building Interpretable Medical Image Diagnostic Models by Dissecting Opaque Neural Networks.- BioSonix: Can Physics-based Sonification Perceptualize Tissue Deformations from Tool Interactions? Brain: Explainable Deep Model for Understanding Neuropathological Events Through Neural Symbolic Regression.- A Multi-Layer Neural Transport Model for Characterizing Pathology Propagation in Neurodegenerative Diseases.- Enhancing Alzheimer's Diagnosis: Leveraging Anatomical Landmarks in Graph Convolutional Neural Networks on Tetrahedral Meshes.- Hierarchical Variable Importance with Statistical Control for Medical Data-Based Prediction.- Disentangle disease-relevant patterns from irrelevant patterns in fMRI analysis using equivariant and contrastive learning. Diffusion models: Continuous Diffusion Model for Self-supervised Denoising and Super-resolution on Fluorescence Microscopy Images.- Self-Supervised Denoising of Diffusion MRI Data with Efficient Collaborative Diffusion Model.- MAD-AD: Masked Diffusion for Unsupervised Brain Anomaly Detection. Self-supervised learning: Taming Masked Image Modeling for Chest X-ray Diagnosis by Incorporating Clinical Visual Priors.- Diffusion MAE: Paving the Way for Representation Learning of Diffusion MRI.- Resolving quantitative MRI model degeneracy in self-supervised machine learning. Vision-language models: Knowledge-enhanced Hyperbolic Language-Image Pretraining for Zero-shot Learning.- Structure Observation Driven Image-Text Contrastive Learning for Computed Tomography Report Generation.- Hierarchical CLIPs for Fine-grained Anatomical Lesion Localization from Whole-body PET/CT Images.- Multi-View and Multi-Scale Alignment for Contrastive Language-Image Pre-training in Mammography.- Interpretable Few-Shot Retinal Disease Diagnosis with Concept-Guided Prompting of Vision-Language Models.- Full Conformal Adaptation of Medical Vision-Language Models.- A Reality Check of Vision-Language Pre-training in Radiology: Have We Progressed Using Text? Shape analysis: ToothForge: Automatic Dental Shape Generation using Synchronized Spectral Embeddings.- LEDA: Log-Euclidean Diffeomorphism Autoencoder for Efficient Statistical Analysis of Diffeomorphisms.- CoRLD: Contrastive Representation Learning of Deformable Shapes in Images. Time-series image analysis: 4DRGS: 4D Radiative Gaussian Splatting for Efficient 3D Vessel Reconstruction from Sparse-View Dynamic DSA Images.- Brightness-Invariant Tracking Estimation in Tagged MRI.- SafeTriage: Facial Video De-identification for Privacy-Preserving Stroke Triage.

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