Machine Learning in Medical Imaging 14th International Workshop, MLMI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings, Part I

Structural MRI Harmonization via Disentangled Latent Energy-Based Style Translation.- Cross-Domain Iterative Network for Simultaneous Denoising, Limited-angle Reconstruction, and Attenuation Correction of Cardiac SPECT.- Arbitrary Reduction of MRI Inter-slice Spacing Using Hierarchical Feature Conditional Diffusion.- Reconstruction of 3D Fetal Brain MRI from 2D Cross-Sectional Acquisitions Using Unsupervised Learning Network.- Robust Unsupervised Super-Resolution of Infant MRI via Dual-Modal Deep Image Prior.- SR4ZCT: Self-supervised Through-plane Resolution Enhancement for CT Images with Arbitrary Resolution and Overlap.- unORANIC: Unsupervised orthogonalization of anatomy and image-characteristic features.- An Investigation of Different Deep Learning Pipelines for GABA-edited MRS Reconstruction.- Towards Abdominal 3-D Scene Rendering from Laparoscopy Surgical Videos using NeRFs.- Brain MRI to PET Synthesis and Amyloid Estimation in Alzheimer’s Disease via 3D Multimodal Contrastive GAN.- Accelerated MRI Reconstruction via Dynamic Deformable Alignment based Transformer.- Deformable Cross-Attention Transformer for Medical Image Registration.- Deformable Cross-Attention Transformer for Medical Image Registration.- Implicitly solved regularization for learning-based image registration.- BHSD: A 3D Brain Hemorrhage Segmentation Dataset.- Contrastive Learning-based Breast Tumor Segmentation in DCE-MRI.- FFPN: Fourier Feature Pyramid Network for Ultrasound Image Segmentation.- Mammo-SAM: Adapting Foundation Segment Anything Model for Automatic Breast Mass Segmentation in Whole Mammograms.- Consistent and Accurate Segmentation for Serial Infant Brain MR Images with Registration Assistance.- Unifying and Personalizing Weakly-supervised Federated Medical Image Segmentation via Adaptive Representation and Aggregation.- Unlocking Fine-Grained Details with Wavelet-based High-Frequency Enhancement in Transformers.- Prostate Segmentation Using Multiparametric and Multiplanar Magnetic Resonance Images.- SPPNet: A Single-Point Prompt Network for Nuclei Image Segmentation.- Automated Coarse-to-fine Segmentation of Thoracic Duct using Anatomy Priors and Topology-guided Curved Planar Reformation.- Leveraging Self-Attention Mechanism in Vision Transformers for Unsupervised Segmentation of Optical Coherence Microscopy White Matter Images.- PE-MED: Prompt Enhancement for Interactive Medical Image Segmentation.- A Super Token Vision Transformer and CNN Parallel Branch Network for mCNV Lesion Segmentation in OCT Images.- Boundary-RL: Reinforcement Learning for Weakly-Supervised Prostate Segmentation in TRUS Images.- A Domain-free Semi-supervised Method for Myocardium Segmentation in 2D Echocardiography Sequences.- Self-Training with Domain-mixed Data for Few-Shot Domain Adaptation in Medical Image Segmentation Tasks.- Bridging the Task Barriers: Online Knowledge Distillation Across Tasks for Semi-Supervised Mediastinal Segmentation in CT.- Relational UNet for Image Segmentation.- Interpretability-guided Data Augmentation for Robust Segmentation in Multi-centre Colonoscopy Data.- Improving Automated Prostate Cancer Detection and Classification Accuracy with Multi-Scale Cancer Information.- Skin Lesion Segmentation Improved by Transformer-based Networks with Inter-Scale Dependency Modeling.- MagNET: Modality-Agnostic Network for Brain Tumor Segmentation and Characterization with Missing Modalities.- Unsupervised Anomaly Detection in Medical Images Using Masked Diffusion Model.- IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease Prediction.- Multi-Modal Adapter for Medical Vision-and-Language Learning.- Sector Quantized Multi-modal Guidance for Alzheimer's Disease Diagnosis Based on Feature Imputation.- Finding-Aware Anatomical Tokens for Chest X-Ray Automated Reporting.- Dual-stream model with brain metrics and images for MRI-based fetal brain age estimation.- PECon: Contrastive Pretraining to Enhance Feature Alignment between CT and EHR Data for Improved Pulmonary Embolism Diagnosis.- Exploring the Transfer Learning Capabilities of CLIP in Domain Generalization for Diabetic Retinopathy.- More From Less: Self-Supervised Knowledge Distillation for Routine Histopathology Data.- Tailoring Large Language Models to Radiology: A preliminary approach to LLM adaptation for a highly specialized domain.