Computational (Integrative) Pathology -- Semi-supervised histological image segmentation via hierarchical consistency enforcement -- Federated Stain Normalization for Computational Pathology -- DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image Classification -- ReMix: A General and Efficient Framework for Multiple Instance Learning based Whole Slide Image Classification -- S3R: Self-supervised Spectral Regression for Hyperspectral Histopathology Image Classification -- Distilling Knowledge from Topological Representations for Pathological Complete Response Prediction -- SETMIL: Spatial Encoding Transformer-based Multiple Instance Learning for Pathological Image Analysis -- Clinical-realistic Annotation for Histopathology Images with Probabilistic Semi-supervision: A Worst-case Study -- End-to-end Learning for Image-based Detection of Molecular Alterations in Digital Pathology -- S5CL: Unifying Fully-Supervised, Self-Supervised, and Semi-Supervised Learning Through Hierarchical Contrastive Learning -- Sample hardness based gradient loss for long-tailed cervical cell detection -- Test-time image-to-image translation ensembling improves out-of-distribution generalization in histopathology -- Predicting molecular traits from tissue morphology through self-interactive multi-instance learning -- InsMix: Towards Realistic Generative Data Augmentation for Nuclei Instance Segmentation -- Improved Domain Generalization for Cell Detection in Histopathology Images via Test-Time Stain Augmentation -- Transformer based multiple instance learning for weakly supervised histopathology image segmentation -- GradMix for nuclei segmentation and classification in imbalanced pathology image datasets -- Spatial-hierarchical Graph Neural Network with Dynamic Structure Learning for Histological Image Classification -- Gigapixel Whole-Slide Images Classification using Locally Supervised Learning -- Whole Slide Cervical Cancer Screening Using Graph Attention Network and Supervised Contrastive Learning -- RandStainNA: Learning Stain-Agnostic Features from Histology Slides by Bridging Stain Augmentation and Normalization -- Identify Consistent Imaging Genomic Biomarkers for Characterizing the Survival-associated Interactions between Tumor-infiltrating Lymphocytes and Tumors -- Semi-Supervised PR Virtual Staining for Breast Histopathological Images -- Benchmarking the Robustness of Deep Neural Networks to Common Corruptions in Digital Pathology -- Weakly Supervised Segmentation by Tensor Graph Learning for Whole Slide Images -- Test Time Transform Prediction for Open Set Histopathological Image Recognition -- Lesion-Aware Contrastive Representation Learning for Histopathology Whole Slide Images Analysis -- Kernel Attention Transformer (KAT) for Histopathology Whole Slide Image Classification -- Joint Region-Attention and Multi-Scale Transformer for Microsatellite Instability Detection from Whole Slide Images in Gastrointestinal Cancer -- Self-Supervised Pre-Training for NucleiSegmentation -- LifeLonger: A Benchmark for Continual Disease Classification -- Unsupervised Nuclei Segmentation using Spatial Organization Priors -- Visual deep learning-based explanation for neuritic plaques segmentation in Alzheimer's Disease using weakly annotated whole slide histopathological images -- MaNi: Maximizing Mutual Information for Nuclei Cross-Domain Unsupervised Segmentation -- Region-guided CycleGANs for Stain Transfer in Whole Slide Images -- Uncertainty Aware Sampling Framework of Weak-Label Learning for Histology Image Classification -- Local Attention Graph-based Transformer for Multi-target Genetic Alteration Prediction -- Incorporating intratumoral heterogeneity into weakly-supervised deep learning models via variance pooling -- Prostate Cancer Histology Synthesis using StyleGAN Latent Space Annotation -- Fast FF-to-FFPE Whole Slide Image Translation via Laplacian Pyramid and Contrastive Learning -- Feature Re-calibration based Multiple Instance Learning for Whole Slide Image Classification -- Computational Anatomy and Physiology -- Physiological Model based Deep Learning Framework for Cardiac TMP Recovery -- DentalPointNet: Landmark Localization on High-Resolution 3D Digital Dental Models -- Landmark-free Statistical Shape Modeling via Neural Flow Deformations -- Learning shape distributions from large databases of healthy organs: applications to zero-shot and few-shot abnormal pancreas detection -- From Images to Probabilistic Anatomical Shapes: A Deep Variational Bottleneck Approach -- Opthalmology -- Structure-consistent Restoration Network for Cataract Fundus Image Enhancement -- Unsupervised Domain Adaptive Fundus Image Segmentation with Category-level Regularization -- Degradation-invariant Enhancement of Fundus Images via Pyramid Constraint Network -- A Spatiotemporal Model for Precise and Efficient Fully-automatic 3D Motion Correction in OCT -- DA-Net: Dual Branch Transformer and Adaptive Strip Upsampling for Retinal Vessels Segmentation -- Visual explanations for the detection of diabetic retinopathy from retinal fundus images -- Multidimensional Hypergraph on Delineated Retinal Features for Pathological Myopia Task -- Unsupervised Lesion-Aware Transfer Learning for Diabetic Retinopathy Grading in Ultra-Wide-Field Fundus Photography -- Local-Region and Cross-Dataset Contrastive Learning for Retinal Vessel Segmentation -- Y-Net: A Spatiospectral Dual-Encoder Network for Medical Image Segmentation -- Camera Adaptation for Fundus-Image-Based CVD Risk Estimation -- Opinions Vary? Diagnosis First! -- Learning self-calibrated optic disc and cup segmentation from multi-rater annotations -- TINC: Temporally Informed Non-Contrastive Learning for Disease Progression Modeling in Retinal OCT Volumes -- DRGen: Domain Generalization in Diabetic Retinopathy Classification -- Frequency-Aware Inverse-Consistent Deep Learning for OCT-Angiogram Super-Resolution -- A Multi-task Network with Weight Decay Skip Connection Training for Anomaly Detection in Retinal Fundus Images -- Multiscale Unsupervised Retinal Edema Area Segmentation in OCT Images -- SeATrans: Learning Segmentation-Assisted diagnosis model via Transformer -- Screening of Dementia on OCTA Images via Multi-projection Consistency and Complementarity -- Noise transfer for unsupervised domain adaptation of retinal OCT images -- Long-tailed Multi-label Retinal Diseases Recognition via Relational Learning and Knowledge Distillation -- Fetal Imaging -- Weakly Supervised Online Action Detection for Infant General Movements -- Super-Focus: Domain Adaptation for Embryo Imaging via Self-Supervised Focal Plane Regression -- SUPER-IVIM-DC: Intra-voxel incoherent motion based Fetal lung maturity assessment from limited DWI data using supervised learning coupled with data-consistency -- Automated Classification of General Movements in Infants Using Two-stream Spatiotemporal Fusion Network.

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. .