{"product_id":"information-processing-in-medical-imaging-9783031966279","title":"Information Processing in Medical Imaging","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp class=\"MsoNormal\"\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\" style=\"mso-ansi-language: EN-US;\"\u003eClassification\/Detection:\u003c\/span\u003e\u003c\/strong\u003e\u003cspan lang=\"EN-US\" style=\"mso-ansi-language: EN-US;\"\u003e 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. \u003cstrong\u003eRegistration:\u003c\/strong\u003e 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. \u003cstrong\u003eReconstruction:\u003c\/strong\u003e 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. \u003cstrong\u003eImage synthesis: \u003c\/strong\u003e3D 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. \u003cstrong\u003eImage enhancement:\u003c\/strong\u003e 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. \u003cstrong\u003eSegmentation:\u003c\/strong\u003e MC-NuSeg: Multi-Contour Aware Nuclei Instance Segmentation with Segment Anything Model.- Pitfalls of topology-aware image segmentation.- GeoT: Geometry-guided Instance-dependent\u003cspan style=\"mso-spacerun: yes;\"\u003e  \u003c\/span\u003eTransition 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.\u003c\/span\u003e\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Default Title","offer_id":53195497898327,"sku":"9783031966279","price":64.99,"currency_code":"GBP","in_stock":true}],"url":"https:\/\/bookcurl.com\/products\/information-processing-in-medical-imaging-9783031966279","provider":"Book Curl","version":"1.0","type":"link"}