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 / edited by Xiaohuan Cao, Xuanang Xu, Islem Rekik, Zhiming Cui, Xi Ouyang.

Format:

Book

Contributor:

Cao, Xiaohuan, editor.

Series:

Lecture Notes in Computer Science, 1611-3349 ; 14348

Language:

English

Subjects (All):

Computer vision.

Image processing--Digital techniques.

Image processing.

Machine learning.

Computer networks.

Social sciences--Data processing.

Social sciences.

Bioinformatics.

Computer Vision.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Machine Learning.

Computer Communication Networks.

Computer Application in Social and Behavioral Sciences.

Local Subjects:

Computer Vision.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Machine Learning.

Computer Communication Networks.

Computer Application in Social and Behavioral Sciences.

Bioinformatics.

Physical Description:

1 online resource (499 pages)

Edition:

1st ed. 2024.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2024.

Summary:

The two-volume set LNCS 14348 and 14139 constitutes the proceedings of the 14th International Workshop on Machine Learning in Medical Imaging, MLMI 2023, held in conjunction with MICCAI 2023, in Vancouver, Canada, in October 2023. The 93 full papers presented in the proceedings were carefully reviewed and selected from 139 submissions. They focus on major trends and challenges in artificial intelligence and machine learning in the medical imaging field, translating medical imaging research into clinical practice. Topics of interests included deep learning, generative adversarial learning, ensemble learning, transfer learning, multi-task learning, manifold learning, reinforcement learning, along with their applications to medical image analysis, computer-aided diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc.

Contents:

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

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.

Notes:

Includes bibliographical references and index.

ISBN:

3-031-45673-4