FedBM: Stealing knowledge from pre-trained language models for heterogeneous federated learning.

Federated learning (FL) has shown great potential in medical image computing since it provides a decentralized learning paradigm that allows multiple clients to train a model collaboratively without privacy leakage. However, current studies have shown that data heterogeneity incurs local learning bias in classifiers and feature extractors of client models during local training, leading to the performance degradation of a federation system. To address these issues, we propose a novel framework called Federated Bias eliMinating (FedBM) to get rid of local learning bias in heterogeneous federated learning (FL), which mainly consists of two modules, i.

Bridging multi-level gaps: Bidirectional reciprocal cycle framework for text-guided label-efficient segmentation in echocardiography.

Text-guided visual understanding is a potential solution for downstream task learning in echocardiography. It can reduce reliance on labeled large datasets and facilitate learning clinical tasks. This is because the text can embed highly condensed clinical information into predictions for visual tasks.

Study-level cross-modal retrieval of chest x-ray images and reports with adapter-based fine-tuning.

Cross-modal retrieval is crucial for improving clinical decision-making and report generation. However, current technologies mainly focus on linking single images with reports, ignoring the need to comprehensively observe multiple images in real clinical environments. Additionally, differences in imaging equipment, scanning parameters, geographic regions, and reporting styles in chest x-rays and reports cause inconsistent data distributions, which challenge model reliability and generalization.

Multimodal Cross Global Learnable Attention Network for MR images denoising with arbitrary modal missing.

Magnetic Resonance Imaging (MRI) generates medical images of multiple sequences, i.e., multimodal, from different contrasts.

Multiple token rearrangement Transformer network with explicit superpixel constraint for segmentation of echocardiography.

Diagnostic cardiologists have considerable clinical demand for precise segmentation of echocardiography to diagnose cardiovascular disease. The paradox is that manual segmentation of echocardiography is a time-consuming and operator-dependent task. Computer-aided segmentation can reduce the workflow greatly.

Artificial intelligence-assisted echocardiographic monitoring in pediatric patients on extracorporeal membrane oxygenation.

Background: Percutaneous extracorporeal membrane oxygenation (ECMO) is administered to pediatric patients with cardiogenic shock or cardiac arrest. The traditional method uses focal echocardiography to complete the left ventricular measurement. However, echocardiographic determination of the ejection fraction (EF) by manual tracing of the endocardial borders is time consuming and operator dependent.

Multi-Level Noise Sampling From Single Image for Low-Dose Tomography Reconstruction.

Low-dose digital radiography (DR) and computed tomography (CT) become increasingly popular due to reduced radiation dose. However, they often result in degraded images with lower signal-to-noise ratios, creating an urgent need for effective denoising techniques. The recent advancement of the single-image-based denoising approach provides a promising solution without requirement of pairwise training data, which are scarce in medical imaging.

Design and implementation of corrosion-resistant multitasking cell stainers.

Compared with machine staining, traditional manual staining faces various problems, such as a low preparation success rate, low efficiency, and harm to the human body due to corrosive gases. Therefore, a stainer that is of low cost, has strong corrosion resistance, and is suitable for small-batch preparation should be developed. In this study, by choosing a rotary scheme as the structural basis, a reusable container cover and a master-slave manipulator cooperation scheme are developed, which greatly improve the space utilization rate.

Main Coronary Flow Calculation With the Assistance of Physiological Side Branch Flow.

Objective: Fractional flow reserve (FFR) derived from coronary angiography, referred to as ICA-FFR, is a less-invasive alternative for invasive FFR measurement based on computational fluid dynamics. Blood flow into side branches influences the accuracy of ICA-FFR. However, properly compensating for side branch flow in ICA-FFR analysis is challenging.

Adaptive dynamic inference for few-shot left atrium segmentation.

Accurate segmentation of the left atrium (LA) from late gadolinium-enhanced cardiac magnetic resonance (LGE CMR) images is crucial for aiding the treatment of patients with atrial fibrillation. Few-shot learning holds significant potential for achieving accurate LA segmentation with low demand on high-cost labeled LGE CMR data and fast generalization across different centers. However, accurate LA segmentation with few-shot learning is a challenging task due to the low-intensity contrast between the LA and other neighboring organs in LGE CMR images.

Prediction of Freezing of Gait in Parkinson's disease based on multi-channel time-series neural network.

Freezing of Gait (FOG) is a noticeable symptom of Parkinson's disease, like being stuck in place and increasing the risk of falls. The wearable multi-channel sensor system is an efficient method to predict and monitor the FOG, thus warning the wearer to avoid falls and improving the quality of life. However, the existing approaches for the prediction of FOG mainly focus on a single sensor system and cannot handle the interference between multi-channel wearable sensors.

Accurate segmentation of liver tumor from multi-modality non-contrast images using a dual-stream multi-level fusion framework.

The use of multi-modality non-contrast images (i.e., T1FS, T2FS and DWI) for segmenting liver tumors provides a solution by eliminating the use of contrast agents and is crucial for clinical diagnosis.

A coronary CT angiography-derived myocardial radiomics model for predicting adverse outcomes in chronic myocardial infarction.

Background: The prognostic efficacy of a coronary computed tomography angiography (CCTA)-derived myocardial radiomics model in patients with chronic myocardial infarction (MI) is unclear.

Methods: In this retrospective study, a cohort of 236 patients with chronic MI who underwent both CCTA and cardiac magnetic resonance (CMR) examinations within 30 days were enrolled and randomly divided into training and testing datasets at a ratio of 7:3. The clinical endpoints were major adverse cardiovascular events (MACE), defined as all-cause death, myocardial reinfarction and heart failure hospitalization.

Constraint-Aware Learning for Fractional Flow Reserve Pullback Curve Estimation From Invasive Coronary Imaging.

Estimation of the fractional flow reserve (FFR) pullback curve from invasive coronary imaging is important for the intraoperative guidance of coronary intervention. Machine/deep learning has been proven effective in FFR pullback curve estimation. However, the existing methods suffer from inadequate incorporation of intrinsic geometry associations and physics knowledge.

Non-Invasive Assessment of Coronary Microvascular Dysfunction Using Vascular Deformation-Based Flow Estimation.

Objective: Non-invasive computation of the index of microcirculatory resistance from coronary computed tomography angiography (CTA), referred to as IMR[Formula: see text], is a promising approach for quantitative assessment of coronary microvascular dysfunction (CMD). However, the computation of IMR[Formula: see text] remains an important unresolved problem due to its high requirement for the accuracy of coronary blood flow. Existing CTA-based methods for estimating coronary blood flow rely on physiological assumption models to indirectly identify, which leads to inadequate personalization of total and vessel-specific flow.

Quantification of functional hemodynamics in aortic valve disease using cardiac computed tomography angiography.

Background And Objective: Cardiac computed tomography angiography (CTA) is the preferred modality for preoperative planning in aortic valve stenosis. However, it cannot provide essential functional hemodynamic data, specifically the mean transvalvular pressure gradient (MPG). This study aims to introduce a computational fluid dynamics (CFD) approach for MPG quantification using cardiac CTA, enhancing its diagnostic value.

Distraction-aware hierarchical learning for vascular structure segmentation in intravascular ultrasound images.

Vascular structure segmentation in intravascular ultrasound (IVUS) images plays an important role in pre-procedural evaluation of percutaneous coronary intervention (PCI). However, vascular structure segmentation in IVUS images has the challenge of structure-dependent distractions. Structure-dependent distractions are categorized into two cases, structural intrinsic distractions and inter-structural distractions.

Frequency-Enhanced Geometric-Constrained Reconstruction for Localizing Myocardial Infarction in 12-Lead Electrocardiograms.

Determining the location of myocardial infarction is crucial for clinical management and therapeutic stratagem. However, existing diagnostic tools either sacrifice ease of use or are limited by their spatial resolution. Addressing this, we aim to refine myocardial infarction localization via surface potential reconstruction of the ventricles in 12-lead electrocardiograms (ECG).

Embedding Tasks Into the Latent Space: Cross-Space Consistency for Multi-Dimensional Analysis in Echocardiography.

Multi-dimensional analysis in echocardiography has attracted attention due to its potential for clinical indices quantification and computer-aided diagnosis. It can utilize various information to provide the estimation of multiple cardiac indices. However, it still has the challenge of inter-task conflict.

Cellular nucleus image-based smarter microscope system for single cell analysis.

Cell imaging technology is undoubtedly a powerful tool for studying single-cell heterogeneity due to its non-invasive and visual advantages. It covers microscope hardware, software, and image analysis techniques, which are hindered by low throughput owing to abundant hands-on time and expertise. Herein, a cellular nucleus image-based smarter microscope system for single-cell analysis is reported to achieve high-throughput analysis and high-content detection of cells.

Iterative Residual Optimization Network for Limited-Angle Tomographic Reconstruction.

Limited-angle tomographic reconstruction is one of the typical ill-posed inverse problems, leading to edge divergence with degraded image quality. Recently, deep learning has been introduced into image reconstruction and achieved great results. However, existing deep reconstruction methods have not fully explored data consistency, resulting in poor performance.

A Deformable Constraint Transport Network for Optimal Aortic Segmentation From CT Images.

Aortic segmentation from computed tomography (CT) is crucial for facilitating aortic intervention, as it enables clinicians to visualize aortic anatomy for diagnosis and measurement. However, aortic segmentation faces the challenge of variable geometry in space, as the geometric diversity of different diseases and the geometric transformations that occur between raw and measured images. Existing constraint-based methods can potentially solve the challenge, but they are hindered by two key issues: inaccurate definition of properties and inappropriate topology of transformation in space.

Deep Learning-based Prediction of Percutaneous Recanalization in Chronic Total Occlusion Using Coronary CT Angiography.

Unlabelled: Background CT is helpful in guiding the revascularization of chronic total occlusion (CTO), but manual prediction scores of percutaneous coronary intervention (PCI) success have challenges. Deep learning (DL) is expected to predict success of PCI for CTO lesions more efficiently. Purpose To develop a DL model to predict guidewire crossing and PCI outcomes for CTO using coronary CT angiography (CCTA) and evaluate its performance compared with manual prediction scores.

Explainable Classification of Benign-Malignant Pulmonary Nodules With Neural Networks and Information Bottleneck.

Computerized tomography (CT) is a clinically primary technique to differentiate benign-malignant pulmonary nodules for lung cancer diagnosis. Early classification of pulmonary nodules is essential to slow down the degenerative process and reduce mortality. The interactive paradigm assisted by neural networks is considered to be an effective means for early lung cancer screening in large populations.