E-grāmata: Medical Image Computing and Computer Assisted Intervention - MICCAI 2022: 25th International Conference, Singapore, September 18-22, 2022, Proceedings, Part II

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 Alzheimers 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.