{"product_id":"information-processing-in-medical-imaging-9783031966248","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;\"\u003eComputer-aided diagnosis\/surgery: \u003c\/span\u003e\u003c\/strong\u003e\u003cspan lang=\"EN-US\" style=\"mso-ansi-language: EN-US;\"\u003eConcepts from Neurons: Building Interpretable Medical Image Diagnostic Models by Dissecting Opaque Neural Networks.- BioSonix: Can Physics-based Sonification Perceptualize Tissue Deformations from Tool Interactions? \u003cstrong\u003eBrain: \u003c\/strong\u003eExplainable 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. \u003cstrong\u003eDiffusion models: \u003c\/strong\u003eContinuous 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. \u003cstrong\u003eSelf-supervised learning: \u003c\/strong\u003eTaming 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. \u003cstrong\u003eVision-language models: \u003c\/strong\u003eKnowledge-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? \u003cstrong\u003eShape analysis: \u003c\/strong\u003eToothForge: 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. \u003cstrong\u003eTime-series image analysis: \u003c\/strong\u003e4DRGS: 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.\u003c\/span\u003e\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Default Title","offer_id":53195498127703,"sku":"9783031966248","price":64.99,"currency_code":"GBP","in_stock":true}],"url":"https:\/\/bookcurl.com\/products\/information-processing-in-medical-imaging-9783031966248","provider":"Book Curl","version":"1.0","type":"link"}