IEE Transactions on Medical Imaging IEE Transactions on Medical Imaging RSS feedThis is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.

Patient-Specific Heart Geometry Modeling for Solid Biomechanics Using Deep Learning
Automated volumetric meshing of patient-specific heart geometry can help expedite various biomechanics studies, such as post-intervention stress estimation. Prior meshing techniques often neglect important modeling characteristics for successful downstream analyses, especially for thin structures like the valve leaflets. In this work, we present DeepCarve (Deep Cardiac Volumetric Mesh): a novel deformation-based deep learning method that automatically generates patient-specific volumetric meshes with high spatial accuracy and element quality. The main novelty in our method is the use of minimally sufficient surface mesh la...
Source: IEE Transactions on Medical Imaging - July 11, 2023 Category: Biomedical Engineering Source Type: research

Coarse–Super-Resolution–Fine Network (CoSF-Net): A Unified End-to-End Neural Network for 4D-MRI With Simultaneous Motion Estimation and Super-Resolution
In this study, we developed a novel deep learning framework called the coarse–super-resolution–fine network (CoSF-Net) to achieve simultaneous motion estimation and super-resolution within a unified model. We designed CoSF-Net by fully excavating the inherent properties of 4D-MRI, with consideration of limited and imperfectly matched training datasets. We conducted extensive experiments on multiple real patient datasets to assess the feasibility and robustness of the developed network. Compared with existing networks and three state-of-the-art conventional algorithms, CoSF-Net not only accurately estimated the deformab...
Source: IEE Transactions on Medical Imaging - July 11, 2023 Category: Biomedical Engineering Source Type: research

Collaborative Multi-Metadata Fusion to Improve the Classification of Lumbar Disc Herniation
Computed tomography (CT) images are the most commonly used radiographic imaging modality for detecting and diagnosing lumbar diseases. Despite many outstanding advances, computer-aided diagnosis (CAD) of lumbar disc disease remains challenging due to the complexity of pathological abnormalities and poor discrimination between different lesions. Therefore, we propose a Collaborative Multi-Metadata Fusion classification network (CMMF-Net) to address these challenges. The network consists of a feature selection model and a classification model. We propose a novel Multi-scale Feature Fusion (MFF) module that can improve the ed...
Source: IEE Transactions on Medical Imaging - July 11, 2023 Category: Biomedical Engineering Source Type: research

Masked Conditional Variational Autoencoders for Chromosome Straightening
Karyotyping is of importance for detecting chromosomal aberrations in human disease. However, chromosomes easily appear curved in microscopic images, which prevents cytogeneticists from analyzing chromosome types. To address this issue, we propose a framework for chromosome straightening, which comprises a preliminary processing algorithm and a generative model called masked conditional variational autoencoders (MC-VAE). The processing method utilizes patch rearrangement to address the difficulty in erasing low degrees of curvature, providing reasonable preliminary results for the MC-VAE. The MC-VAE further straightens the...
Source: IEE Transactions on Medical Imaging - July 10, 2023 Category: Biomedical Engineering Source Type: research

FedOSS: Federated Open Set Recognition via Inter-Client Discrepancy and Collaboration
Open set recognition (OSR) aims to accurately classify known diseases and recognize unseen diseases as the unknown class in medical scenarios. However, in existing OSR approaches, gathering data from distributed sites to construct large-scale centralized training datasets usually leads to high privacy and security risk, which could be alleviated elegantly via the popular cross-site training paradigm, federated learning (FL). To this end, we represent the first effort to formulate federated open set recognition (FedOSR), and meanwhile propose a novel Federated Open Set Synthesis (FedOSS) framework to address the core challe...
Source: IEE Transactions on Medical Imaging - July 10, 2023 Category: Biomedical Engineering Source Type: research

Deep Generalized Learning Model for PET Image Reconstruction
Low-count positron emission tomography (PET) imaging is challenging because of the ill-posedness of this inverse problem. Previous studies have demonstrated that deep learning (DL) holds promise for achieving improved low-count PET image quality. However, almost all data-driven DL methods suffer from fine structure degradation and blurring effects after denoising. Incorporating DL into the traditional iterative optimization model can effectively improve its image quality and recover fine structures, but little research has considered the full relaxation of the model, resulting in the performance of this hybrid model not be...
Source: IEE Transactions on Medical Imaging - July 10, 2023 Category: Biomedical Engineering Source Type: research

Equilibrated Zeroth-Order Unrolled Deep Network for Parallel MR Imaging
In recent times, model-driven deep learning has evolved an iterative algorithm into a cascade network by replacing the regularizer’s first-order information, such as the (sub)gradient or proximal operator, with a network module. This approach offers greater explainability and predictability compared to typical data-driven networks. However, in theory, there is no assurance that a functional regularizer exists whose first-order information matches the substituted network module. This implies that the unrolled network output may not align with the regularization models. Furthermore, there are few established theories that ...
Source: IEE Transactions on Medical Imaging - July 10, 2023 Category: Biomedical Engineering Source Type: research

LViT: Language Meets Vision Transformer in Medical Image Segmentation
Deep learning has been widely used in medical image segmentation and other aspects. However, the performance of existing medical image segmentation models has been limited by the challenge of obtaining sufficient high-quality labeled data due to the prohibitive data annotation cost. To alleviate this limitation, we propose a new text-augmented medical image segmentation model LViT (Language meets Vision Transformer). In our LViT model, medical text annotation is incorporated to compensate for the quality deficiency in image data. In addition, the text information can guide to generate pseudo labels of improved quality in t...
Source: IEE Transactions on Medical Imaging - July 3, 2023 Category: Biomedical Engineering Source Type: research

2023 IEEE Nuclear Science Symposium
(Source: IEE Transactions on Medical Imaging)
Source: IEE Transactions on Medical Imaging - July 1, 2023 Category: Biomedical Engineering Source Type: research

A New Framework of Swarm Learning Consolidating Knowledge From Multi-Center Non-IID Data for Medical Image Segmentation
Large training datasets are important for deep learning-based methods. For medical image segmentation, it could be however difficult to obtain large number of labeled training images solely from one center. Distributed learning, such as swarm learning, has the potential to use multi-center data without breaching data privacy. However, data distributions across centers can vary a lot due to the diverse imaging protocols and vendors (known as feature skew). Also, the regions of interest to be segmented could be different, leading to inhomogeneous label distributions (referred to as label skew). With such non-independently an...
Source: IEE Transactions on Medical Imaging - July 1, 2023 Category: Biomedical Engineering Source Type: research

IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentation
Federated learning (FL) allows multiple medical institutions to collaboratively learn a global model without centralizing client data. It is difficult, if possible at all, for such a global model to commonly achieve optimal performance for each individual client, due to the heterogeneity of medical images from various scanners and patient demographics. This problem becomes even more significant when deploying the global model to unseen clients outside the FL with unseen distributions not presented during federated training. To optimize the prediction accuracy of each individual client for medical imaging tasks, we propose ...
Source: IEE Transactions on Medical Imaging - July 1, 2023 Category: Biomedical Engineering Source Type: research

Multi-Task Distributed Learning Using Vision Transformer With Random Patch Permutation
The widespread application of artificial intelligence in health research is currently hampered by limitations in data availability. Distributed learning methods such as federated learning (FL) and split learning (SL) are introduced to solve this problem as well as data management and ownership issues with their different strengths and weaknesses. The recent proposal of federated split task-agnostic (F eSTA) learning tries to reconcile the distinct merits of FL and SL by enabling the multi-task collaboration between participants through Vision Transformer (ViT) architecture, but they suffer from higher communication overhea...
Source: IEE Transactions on Medical Imaging - July 1, 2023 Category: Biomedical Engineering Source Type: research

A Dataset Auditing Method for Collaboratively Trained Machine Learning Models
Dataset auditing for machine learning (ML) models is a method to evaluate if a given dataset is used in training a model. In a Federated Learning setting where multiple institutions collaboratively train a model with their decentralized private datasets, dataset auditing can facilitate the enforcement of regulations, which provide rules for preserving privacy, but also allow users to revoke authorizations and remove their data from collaboratively trained models. This paper first proposes a set of requirements for a practical dataset auditing method, and then present a novel dataset auditing method called Ensembled Members...
Source: IEE Transactions on Medical Imaging - July 1, 2023 Category: Biomedical Engineering Source Type: research

Federated Active Learning for Multicenter Collaborative Disease Diagnosis
Current computer-aided diagnosis system with deep learning method plays an important role in the field of medical imaging. The collaborative diagnosis of diseases by multiple medical institutions has become a popular trend. However, large scale annotations put heavy burdens on medical experts. Furthermore, the centralized learning system has defects in privacy protection and model generalization. To meet these challenges, we propose two federated active learning methods for multicenter collaborative diagnosis of diseases: the Labeling Efficient Federated Active Learning (LEFAL) and the Training Efficient Federated Active L...
Source: IEE Transactions on Medical Imaging - July 1, 2023 Category: Biomedical Engineering Source Type: research

Federated Learning With Privacy-Preserving Ensemble Attention Distillation
Federated Learning (FL) is a machine learning paradigm where many local nodes collaboratively train a central model while keeping the training data decentralized. This is particularly relevant for clinical applications since patient data are usually not allowed to be transferred out of medical facilities, leading to the need for FL. Existing FL methods typically share model parameters or employ co-distillation to address the issue of unbalanced data distribution. However, they also require numerous rounds of synchronized communication and, more importantly, suffer from a privacy leakage risk. We propose a privacy-preservin...
Source: IEE Transactions on Medical Imaging - July 1, 2023 Category: Biomedical Engineering Source Type: research