Brain Development and Atlases -- Progression models for imaging data with Longitudinal Variational Auto Encoders -- Boundary-Enhanced Self-Supervised Learning for Brain Structure Segmentation -- Domain-Prior-Induced Structural MRI Adaptation for Clinical Progression Prediction of Subjective Cognitive Decline -- 3D Global Fourier Network for Alzheimer's Disease Diagnosis using Structural MRI -- CASHformer: Cognition Aware SHape Transformer for Longitudinal Analysis -- Interpretable differential diagnosis for Alzheimer's disease and Frontotemporal dementia -- Is a PET all you need? A multi-modal study for Alzheimer's disease using 3D CNNs -- Unsupervised Representation Learning of Cingulate Cortical Folding Patterns -- Feature robustness and sex differences in medical imaging: a case study in MRI-based Alzheimer's disease detection -- Extended Electrophysiological Source Imaging with Spatial Graph Filters -- DWI and Tractography -- Hybrid Graph Transformer for Tissue Microstructure Estimation with Undersampled Diffusion MRI Data -- Atlas-powered deep learning (ADL) - application to diffusion weighted MRI -- One-Shot Segmentation of Novel White Matter Tracts via Extensive Data Augmentation -- Accurate Corresponding Fiber Tract Segmentation via FiberGeoMap Learner -- An adaptive network with extragradient for diffusion MRI-based microstructure estimation -- Shape-based features of white matter fiber-tracts associated with outcome in Major Depression Disorder -- White Matter Tracts are Point Clouds: Neuropsychological Score Prediction and Critical Region Localization via Geometric Deep Learning -- Segmentation of Whole-brain Tractography: A Deep Learning Algorithm Based on 3D Raw Curve Points -- TractoFormer: A Novel Fiber-level Whole Brain Tractography Analysis Framework Using Spectral Embedding and Vision Transformers -- Multi-site Normative Modeling of Diffusion Tensor Imaging Metrics Using Hierarchical Bayesian Regression -- Functional Brain Networks -- Contrastive Functional Connectivity Graph Learning for Population-based fMRI Classification -- Joint Graph Convolution for Analyzing Brain Structural and Functional Connectome -- Decoding Task Sub-type States with Group Deep Bidirectional Recurrent Neural Network -- Hierarchical Brain Networks Decomposition via Prior Knowledge Guided Deep Belief Network -- Interpretable signature of consciousness in resting-state functional network brain activity -- Nonlinear Conditional Time-varying Granger Causality of Task fMRI via Deep Stacking Networks and Adaptive Convolutional Kernels -- fMRI Neurofeedback Learning Patterns are Predictive of Personal and Clinical Traits -- Multi-head Attention-based Masked Sequence Model for Mapping Functional Brain Networks -- Dual-HINet: Dual Hierarchical Integration Network of Multigraphs for Connectional Brain Template Learning -- RefineNet: An Automated Framework to Generate Task and Subject-Specific Brain Parcellations for Resting-State fMRI Analysis -- Modelling Cycles in Brain Networks with the Hodge Laplacian -- Predicting Spatio-Temporal Human Brain Response Using fMRI -- Revealing Continuous Brain Dynamical Organization with Multimodal Graph Transformer -- Explainable Contrastive Multiview Graph Representation of Brain, Mind, and Behavior -- Embedding Human Brain Function via Transformer -- How Much to Aggregate: Learning Adaptive Node-wise Scales on Graphs for Brain Networks -- Combining multiple atlases to estimate data-driven mappings between functional connectomes using optimal transport -- The Semi-constrained Network-Based Statistic (scNBS): integrating local and global information for brain network inference -- Unified Embeddings of Structural and Functional Connectome via a Function-Constrained Structural Graph Variational Auto-Encoder -- Neuroimaging -- Characterization of brain activity patterns across states of consciousness based on variational auto-encoders -- Conditional VAEs for confound removal and normative modelling of neurodegenerative diseases -- Semi-supervised learning with data harmonisation for biomarker discovery from resting state fMRI -- Cerebral Microbleeds Detection Using a 3D Feature Fused Region Proposal Network with Hard Sample Prototype Learning -- Brain-Aware Replacements for Supervised Contrastive Learning in Detection of Alzheimer's Disease -- Heart and Lung Imaging -- AANet: Artery-Aware Network for Pulmonary Embolism Detection in CTPA Images -- Siamese Encoder-based Spatial-Temporal Mixer for Growth Trend Prediction of Lung Nodules on CT Scans -- What Makes for Automatic Reconstruction of Pulmonary Segments -- CFDA: Collaborative Feature Disentanglement and Augmentation for Pulmonary Airway Tree Modeling of COVID-19 CTs -- Decoupling Predictions in Distributed Learning for Multi-Center Left Atrial MRI Segmentation -- Scribble-Supervised Medical Image Segmentation via Dual-Branch Network and Dynamically Mixed Pseudo Labels Supervision -- Diffusion Deformable Model for 4D Temporal Medical Image Generation -- SAPJNet: Sequence-Adaptive Prototype-Joint Network for Small Sample Multi-Sequence MRI Diagnosis -- Evolutionary Multi-objective Architecture Search Framework: Application to COVID-19 3D CT Classification -- Detecting Aortic Valve Pathology from the 3-Chamber Cine Cardiac MRI View -- CheXRelNet: An Anatomy-Aware Model for Tracking Longitudinal Relationships between Chest X-Rays -- Reinforcement learning for active modality selection during diagnosis -- Ensembled Prediction of Rheumatic Heart Disease from Ungated Doppler Echocardiography Acquired in Low-Resource Settings -- Attention mechanisms for physiological signal deep learning: which attention should we take? -- Computer-aided Tuberculosis Diagnosis with Attribute Reasoning Assistance -- Multimodal Contrastive Learning for Prospective Personalized Estimation of CT Organ Dose -- RTN: Reinforced Transformer Network for Coronary CT Angiography Vessel-level Image Quality Assessment -- A Comprehensive Study of Modern Architectures and Regularization Approaches on CheXpert5000.-LSSANet: A Long Short Slice-Aware Network for Pulmonary Nodule Detection -- Consistency-based Semi-supervised Evidential Active Learning for Diagnostic Radiograph Classification -- Self-Rating Curriculum Learning for Localization and Segmentation of Tuberculosis on Chest Radiograph -- Rib Suppression in Digital Chest Tomosynthesis -- Multi-Task Lung Nodule Detection in Chest Radiographs with a Dual Head Network -- Dermatology -- Data-Driven Deep Supervision for Skin Lesion Classification -- Out-of-Distribution Detection for Long-tailed and Fine-grained Skin Lesion Images -- FairPrune: Achieving Fairness Through Pruning for Dermatological Disease Diagnosis -- Reliability-aware Contrastive Self-ensembling for Semi-supervised Medical Image Classification.

The eight-volume set LNCS 13431, 13432, 13433, 13434, 13435, 13436, 13437, and 13438 constitutes the refereed proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which was held in Singapore in September 2022. The 574 revised full papers presented were carefully reviewed and selected from 1831 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: Brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; heart and lung imaging; dermatology; Part II: Computational (integrative) pathology; computational anatomy and physiology; ophthalmology; fetal imaging; Part III: Breast imaging; colonoscopy; computer aided diagnosis; Part IV: Microscopic image analysis; positron emission tomography; ultrasound imaging; video data analysis; image segmentation I; Part V: Image segmentation II; integration of imaging with non-imaging biomarkers; Part VI: Image registration; image reconstruction; Part VII: Image-Guided interventions and surgery; outcome and disease prediction; surgical data science; surgical planning and simulation; machine learning - domain adaptation and generalization; Part VIII: Machine learning - weakly-supervised learning; machine learning - model interpretation; machine learning - uncertainty; machine learning theory and methodologies. .