Beställningsvara. Skickas inom 7-10 vardagar. Fri frakt över 249 kr.
Beskrivning
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.
Classification/Detection: SpectMamba: Integrating Frequency and State Space Models for Enhanced Medical Image Detection.- Hierarchical Neural Cellular Automata for Lightweight Microscopy Image Classification.- PathTTT: Test-Time Training with Meta-Auxiliary Learning for Pathology Image Classification. Registration: Bi-invariant Geodesic Regression with Data from the Osteoarthritis Initiative.- GSSD: A Self-Distillation Paradigm with Gradient Surgery for End-to-End Deformable Image Registration.- Medical Image Registration Meets Vision Foundation Model: Prototype Learning and Contour Awareness.- Vascular-topology-aware Deep Structure Matching for 2D DSA and 3D CTA Rigid Registration.- Unsupervised Deformable Image Registration with Structural Nonparametric Smoothing. Reconstruction: Unsupervised Accelerated MRI Reconstruction via Ground-Truth-Free Flow Matching.- Optimization of acquisition schemes towards a better estimation of microstructure parameters in multidimensional diffusion MRI.- Bilinear Projector: Mitigating Discretization Artifacts in Model Based Iterative Reconstruction for X-ray CT.- Subspace Implicit Neural Representations for Real-Time Cardiac Cine MR Imaging. Image synthesis: 3D Shape-to-Image Brownian Bridge Diffusion for Brain MRI Synthesis from Cortical Surfaces.- Cascaded Diffusion Model and Segment Anything Model for Medical Image Synthesis via Uncertainty-Guided Prompt Generation.- DIReCT: Domain-Informed Rectified Flow for Controllable Brain MRI to PET Translation.- IGG: Image Generation Informed by Geodesic Dynamics in Deformation Spaces. Image enhancement: Cycle-consistent zero-shot through-plane super-resolution for anisotropic head MRI.- Bayesian Learning with Stochastic Perturbations and Langevin Expectation Maximization for Unsupervised DNN Image Quality Enhancement. Segmentation: MC-NuSeg: Multi-Contour Aware Nuclei Instance Segmentation with Segment Anything Model.- Pitfalls of topology-aware image segmentation.- GeoT: Geometry-guided Instance-dependent Transition Matrix for Semi-supervised Tooth Point Cloud Segmentation.- RemInD: Remembering Anatomical Variations for Interpretable Domain Adaptive Medical Image Segmentation.- Dynamic Allocation Hypernetwork with Adaptive Model Recalibration for Federated Continual Learning.- SkeIite: Compact Neural Networks for Efficient Iterative Skeletonization.- VerSe: Integrating Multiple Queries as Prompts for Versatile Cardiac MRI Segmentation.
Danail Stoyanov, Zeike Taylor, Seyed Mostafa Kia, Ipek Oguz, Mauricio Reyes, Anne Martel, Lena Maier-Hein, Andre F. Marquand, Edouard Duchesnay, Tommy Löfstedt, Bennett Landman, M. Jorge Cardoso, Carlos A. Silva, Sergio Pereira, Raphael Meier
Henning Müller, B. Michael Kelm, Tal Arbel, Weidong Cai, M. Jorge Cardoso, Georg Langs, Bjoern Menze, Dimitris Metaxas, Albert Montillo, William M. Wells III, Shaoting Zhang, Albert C.S. Chung, Mark Jenkinson, Annemie Ribbens