Poster Session 1
P1 - P46
13:30 - 14:15
P-1: Structural MRI Harmonization via Disentangled Latent Energy-Based Style Translation
P-1: Structural MRI Harmonization via Disentangled Latent Energy-Based Style Translation
P-2: Cross-Domain Iterative Network for Simultaneous Denoising, Limited-angle Reconstruction, and Attenuation Correction of Cardiac SPECT
P-2: Cross-Domain Iterative Network for Simultaneous Denoising, Limited-angle Reconstruction, and Attenuation Correction of Cardiac SPECT
P-3: Arbitrary Reduction of MRI Inter-slice Spacing Using Hierarchical Feature Conditional Diffusion
P-3: Arbitrary Reduction of MRI Inter-slice Spacing Using Hierarchical Feature Conditional Diffusion
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-4: Reconstruction of 3D Fetal Brain MRI from 2D Cross-Sectional Acquisitions Using Unsupervised Learning Network
P-4: Reconstruction of 3D Fetal Brain MRI from 2D Cross-Sectional Acquisitions Using Unsupervised Learning Network
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-5: Robust Unsupervised Super-Resolution of Infant MRI via Dual-Modal Deep Image Prior
P-5: Robust Unsupervised Super-Resolution of Infant MRI via Dual-Modal Deep Image Prior
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-6: SR4ZCT: Self-supervised Through-plane Resolution Enhancement for CT Images with Arbitrary Resolution and Overlap
P-6: SR4ZCT: Self-supervised Through-plane Resolution Enhancement for CT Images with Arbitrary Resolution and Overlap
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-7: unORANIC: Unsupervised orthogonalization of anatomy and image-characteristic features
P-7: unORANIC: Unsupervised orthogonalization of anatomy and image-characteristic features
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-8: An Investigation of Different Deep Learning Pipelines for GABA-edited MRS Reconstruction
P-8: An Investigation of Different Deep Learning Pipelines for GABA-edited MRS Reconstruction
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-9: Towards Abdominal 3-D Scene Rendering from Laparoscopy Surgical Videos using NeRFs
P-9: Towards Abdominal 3-D Scene Rendering from Laparoscopy Surgical Videos using NeRFs
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-10: Brain MRI to PET Synthesis and Amyloid Estimation in Alzheimer’s Disease via 3D Multimodal Contrastive GAN
P-10: Brain MRI to PET Synthesis and Amyloid Estimation in Alzheimer’s Disease via 3D Multimodal Contrastive GAN
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-11: Accelerated MRI Reconstruction via Dynamic Deformable Alignment based Transformer
P-11: Accelerated MRI Reconstruction via Dynamic Deformable Alignment based Transformer
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-12: Deformable Cross-Attention Transformer for Medical Image Registration
P-12: Deformable Cross-Attention Transformer for Medical Image Registration
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-13: Deformable Medical Image Registration Under Distribution Shifts with Neural Instance Optimization
P-13: Deformable Medical Image Registration Under Distribution Shifts with Neural Instance Optimization
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-14: Implicitly solved regularization for learning-based image registration
P-14: Implicitly solved regularization for learning-based image registration
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-15: BHSD: A 3D Brain Hemorrhage Segmentation Dataset
P-15: BHSD: A 3D Brain Hemorrhage Segmentation Dataset
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-16: Contrastive Learning-based Breast Tumor Segmentation in DCE-MRI
P-16: Contrastive Learning-based Breast Tumor Segmentation in DCE-MRI
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-17: FFPN: Fourier Feature Pyramid Network for Ultrasound Image Segmentation
P-17: FFPN: Fourier Feature Pyramid Network for Ultrasound Image Segmentation
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-18: Mammo-SAM: Adapting Foundation Segment Anything Model for Automatic Breast Mass Segmentation in Whole Mammograms
P-18: Mammo-SAM: Adapting Foundation Segment Anything Model for Automatic Breast Mass Segmentation in Whole Mammograms
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-19: Consistent and Accurate Segmentation for Serial Infant Brain MR Images with Registration Assistance
P-19: Consistent and Accurate Segmentation for Serial Infant Brain MR Images with Registration Assistance
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-20: Unifying and Personalizing Weakly-supervised Federated Medical Image Segmentation via Adaptive Representation and Aggregation
P-20: Unifying and Personalizing Weakly-supervised Federated Medical Image Segmentation via Adaptive Representation and Aggregation
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-21: Unlocking Fine-Grained Details with Wavelet-based High-Frequency Enhancement in Transformers
P-21: Unlocking Fine-Grained Details with Wavelet-based High-Frequency Enhancement in Transformers
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-22: Prostate Segmentation Using Multiparametric and Multiplanar Magnetic Resonance Images
P-22: Prostate Segmentation Using Multiparametric and Multiplanar Magnetic Resonance Images
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-23: SPPNet: A Single-Point Prompt Network for Nuclei Image Segmentation
P-23: SPPNet: A Single-Point Prompt Network for Nuclei Image Segmentation
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-24: Automated Coarse-to-fine Segmentation of Thoracic Duct using Anatomy Priors and Topology-guided Curved Planar Reformation
P-24: Automated Coarse-to-fine Segmentation of Thoracic Duct using Anatomy Priors and Topology-guided Curved Planar Reformation
P-25: Leveraging Self-Attention Mechanism in Vision Transformers for Unsupervised Segmentation of Optical Coherence Microscopy White Matter Images
P-25: Leveraging Self-Attention Mechanism in Vision Transformers for Unsupervised Segmentation of Optical Coherence Microscopy White Matter Images
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-26: PE-MED: Prompt Enhancement for Interactive Medical Image Segmentation
P-26: PE-MED: Prompt Enhancement for Interactive Medical Image Segmentation
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-27: A Super Token Vision Transformer and CNN Parallel Branch Network for mCNV Lesion Segmentation in OCT Images
P-27: A Super Token Vision Transformer and CNN Parallel Branch Network for mCNV Lesion Segmentation in OCT Images
![](https://www.google.com/images/icons/product/drive-32.png)
P-28: Boundary-RL: Reinforcement Learning for Weakly-Supervised Prostate Segmentation in TRUS Images
P-28: Boundary-RL: Reinforcement Learning for Weakly-Supervised Prostate Segmentation in TRUS Images
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-29: A Domain-free Semi-supervised Method for Myocardium Segmentation in 2D Echocardiography Sequences
P-29: A Domain-free Semi-supervised Method for Myocardium Segmentation in 2D Echocardiography Sequences
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-30: Self-Training with Domain-mixed Data for Few-Shot Domain Adaptation in Medical Image Segmentation Tasks
P-30: Self-Training with Domain-mixed Data for Few-Shot Domain Adaptation in Medical Image Segmentation Tasks
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-31: Bridging the Task Barriers: Online Knowledge Distillation Across Tasks for Semi-Supervised Mediastinal Segmentation in CT
P-31: Bridging the Task Barriers: Online Knowledge Distillation Across Tasks for Semi-Supervised Mediastinal Segmentation in CT
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-32: Relational UNet for Image Segmentation
P-32: Relational UNet for Image Segmentation
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-33: Interpretability-guided Data Augmentation for Robust Segmentation in Multi-centre Colonoscopy Data
P-33: Interpretability-guided Data Augmentation for Robust Segmentation in Multi-centre Colonoscopy Data
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-34: Improving Automated Prostate Cancer Detection and Classification Accuracy with Multi-Scale Cancer Information
P-34: Improving Automated Prostate Cancer Detection and Classification Accuracy with Multi-Scale Cancer Information
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-35: Skin Lesion Segmentation Improved by Transformer-based Networks with Inter-Scale Dependency Modeling
P-35: Skin Lesion Segmentation Improved by Transformer-based Networks with Inter-Scale Dependency Modeling
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-36: MagNET: Modality-Agnostic Network for Brain Tumor Segmentation and Characterization with Missing Modalities
P-36: MagNET: Modality-Agnostic Network for Brain Tumor Segmentation and Characterization with Missing Modalities
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-37: Unsupervised Anomaly Detection in Medical Images Using Masked Diffusion Model
P-37: Unsupervised Anomaly Detection in Medical Images Using Masked Diffusion Model
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-38: IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease Prediction
P-38: IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease Prediction
P-39: Multi-Modal Adapter for Medical Vision-and-Language Learning
P-39: Multi-Modal Adapter for Medical Vision-and-Language Learning
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-40: Vector Quantized Multi-modal Guidance for Alzheimer's Disease Diagnosis Based on Feature Imputation
P-40: Vector Quantized Multi-modal Guidance for Alzheimer's Disease Diagnosis Based on Feature Imputation
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-41: Finding-Aware Anatomical Tokens for Chest X-Ray Automated Reporting
P-41: Finding-Aware Anatomical Tokens for Chest X-Ray Automated Reporting
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-42: Dual-stream model with brain metrics and images for MRI-based fetal brain age estimation
P-42: Dual-stream model with brain metrics and images for MRI-based fetal brain age estimation
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-43: PECon: Contrastive Pretraining to Enhance Feature Alignment between CT and EHR Data for Improved Pulmonary Embolism Diagnosis
P-43: PECon: Contrastive Pretraining to Enhance Feature Alignment between CT and EHR Data for Improved Pulmonary Embolism Diagnosis
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-44: Exploring the Transfer Learning Capabilities of CLIP in Domain Generalization for Diabetic Retinopathy
P-44: Exploring the Transfer Learning Capabilities of CLIP in Domain Generalization for Diabetic Retinopathy
![](https://www.google.com/images/icons/product/drive-32.png)
![](https://www.google.com/images/icons/product/drive-32.png)
P-45: More From Less: Self-Supervised Knowledge Distillation for Routine Histopathology Data
P-45: More From Less: Self-Supervised Knowledge Distillation for Routine Histopathology Data
![](https://www.google.com/images/icons/product/drive-32.png)
P-46: Tailoring Large Language Models to Radiology: A preliminary approach to LLM adaptation for a highly specialized domain
P-46: Tailoring Large Language Models to Radiology: A preliminary approach to LLM adaptation for a highly specialized domain
![](https://www.google.com/images/icons/product/drive-32.png)