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Found 43 papers, 20 papers with code
General Purpose Image Encoder DINOv2 for Medical Image Registration
In this paper, we present a training-free deformable image registration method, DINO-Reg, leveraging a general purpose image encoder DINOv2 for image feature extraction.
Multimodal Neurodegenerative Disease Subtyping Explained by ChatGPT
Thus, we propose a multimodal framework that uses early-stage indicators such as imaging, genetics and clinical assessments to classify AD patients into subtypes at early stages.
Spectral Adversarial MixUp for Few-Shot Unsupervised Domain Adaptation
To accomplish this challenging task, first, a spectral sensitivity map is introduced to characterize the generalization weaknesses of models in the frequency domain.
Fact-Checking of AI-Generated Reports
Future generative AI approaches can use the resulting tool to validate their reports leading to a more responsible use of AI in expediting clinical workflows.
Evaluating Large Language Models for Radiology Natural Language Processing
The rise of large language models (LLMs) has marked a pivotal shift in the field of natural language processing (NLP).
Soft-tissue Driven Craniomaxillofacial Surgical Planning
Our framework consists of a bony planner network that estimates the bony movements required to achieve the desired facial outcome and a facial simulator network that can simulate the possible facial changes resulting from the estimated bony movement plans.
Artificial General Intelligence for Medical Imaging
In this review, we explore the potential applications of Artificial General Intelligence (AGI) models in healthcare, focusing on foundational Large Language Models (LLMs), Large Vision Models, and Large Multimodal Models.
Specialty-Oriented Generalist Medical AI for Chest CT Screening
Modern medical records include a vast amount of multimodal free text clinical data and imaging data from radiology, cardiology, and digital pathology.
When Neural Networks Fail to Generalize? A Model Sensitivity Perspective
Domain generalization (DG) aims to train a model to perform well in unseen domains under different distributions.
Distance Map Supervised Landmark Localization for MR-TRUS Registration
We then use the predicted landmarks to generate the affine transformation matrix, which outperforms the clinicians' manual rigid registration by a significant margin in terms of TRE.
Deep Learning-based Facial Appearance Simulation Driven by Surgically Planned Craniomaxillofacial Bony Movement
In this work, we propose an Attentive Correspondence assisted Movement Transformation network (ACMT-Net) to estimate the facial appearance by transforming the bony movement to facial soft tissue through a point-to-point attentive correspondence matrix.
Regression Metric Loss: Learning a Semantic Representation Space for Medical Images
Regression plays an essential role in many medical imaging applications for estimating various clinical risk or measurement scores.
Federated Multi-organ Segmentation with Inconsistent Labels
Extensive experiments on six public abdominal CT datasets show that our Fed-MENU method can effectively obtain a federated learning model using the partially labeled datasets with superior performance to other models trained by either localized or centralized learning methods.
Federated Cross Learning for Medical Image Segmentation
In this paper, we first conduct a theoretical analysis on the FL algorithm to reveal the problem of model aggregation during training on non-iid data.
X-ray Dissectography Improves Lung Nodule Detection
Although radiographs are the most frequently used worldwide due to their cost-effectiveness and widespread accessibility, the structural superposition along the x-ray paths often renders suspicious or concerning lung nodules difficult to detect.
End-to-end Ultrasound Frame to Volume Registration
Fusing intra-operative 2D transrectal ultrasound (TRUS) image with pre-operative 3D magnetic resonance (MR) volume to guide prostate biopsy can significantly increase the yield.
Cross-modal Attention for MRI and Ultrasound Volume Registration
In the past few years, convolutional neural networks (CNNs) have been proved powerful in extracting image features crucial for image registration.
AnaXNet: Anatomy Aware Multi-label Finding Classification in Chest X-ray
Radiologists usually observe anatomical regions of chest X-ray images as well as the overall image before making a decision.
Task-Oriented Low-Dose CT Image Denoising
The extensive use of medical CT has raised a public concern over the radiation dose to the patient.
Deep Neural Networks for the Assessment of Surgical Skills: A Systematic Review
Here, we use the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to systematically survey the literature on the use of Deep Neural Networks for automated and objective surgical skill assessment, with a focus on kinematic data as putative markers of surgical competency.
Noise Entangled GAN For Low-Dose CT Simulation
We propose a Noise Entangled GAN (NE-GAN) for simulating low-dose computed tomography (CT) images from a higher dose CT image.
Robustified Domain Adaptation
Unsupervised domain adaptation (UDA) is widely used to transfer knowledge from a labeled source domain to an unlabeled target domain with different data distribution.
Transducer Adaptive Ultrasound Volume Reconstruction
However, such algorithms are specific to particular transducers and scanning trajectories associated with the training data, which may not be generalized to other image acquisition settings.
Deep Learning Predicts Cardiovascular Disease Risks from Lung Cancer Screening Low Dose Computed Tomography
Cancer patients have a higher risk of cardiovascular disease (CVD) mortality than the general population.
Stabilizing Deep Tomographic Reconstruction
ACID synergizes a deep reconstruction network trained on big data, kernel awareness from CS-inspired processing, and iterative refinement to minimize the data residual relative to real measurement.
Integrative Analysis for COVID-19 Patient Outcome Prediction
Management of high-risk patients with early intervention is a key to lower the fatality rate of COVID-19 pneumonia, as a majority of patients recover naturally.
Sensorless Freehand 3D Ultrasound Reconstruction via Deep Contextual Learning
Transrectal ultrasound (US) is the most commonly used imaging modality to guide prostate biopsy and its 3D volume provides even richer context information.
Self-supervised Training of Graph Convolutional Networks
Furthermore, the adjacency matrix is usually pre-defined and stationary, which makes the data augmentation strategies cannot be employed on the constructed graph structures data to augment the amount of training data.
Multi-organ Segmentation over Partially Labeled Datasets with Multi-scale Feature Abstraction
Shortage of fully annotated datasets has been a limiting factor in developing deep learning based image segmentation algorithms and the problem becomes more pronounced in multi-organ segmentation.
OASIS: One-pass aligned Atlas Set for Image Segmentation
Furthermore, instead of using image based similarity for label fusion, which can be distracted by the large background areas, we propose a novel strategy to compute the label similarity based weights for label fusion.
Multi-hop Convolutions on Weighted Graphs
To address the above weaknesses, in this paper, we propose a new method of multi-hop convolutional network on weighted graphs.
Unified Multi-scale Feature Abstraction for Medical Image Segmentation
Automatic medical image segmentation, an essential component of medical image analysis, plays an importantrole in computer-aided diagnosis.
Feature Fusion Encoder Decoder Network For Automatic Liver Lesion Segmentation
Liver lesion segmentation is a difficult yet critical task for medical image analysis.
Deep Learning in Medical Image Registration: A Survey
This survey, therefore, outlines the evolution of deep learning based medical image registration in the context of both research challenges and relevant innovations in the past few years.
Boundary-weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation
To make the network more sensitive to the boundaries during segmentation, a boundary-weighted segmentation loss (BWL) is proposed.
Knowledge-based Analysis for Mortality Prediction from CT Images
Recent studies have highlighted the high correlation between cardiovascular diseases (CVD) and lung cancer, and both are associated with significant morbidity and mortality.
On a Sparse Shortcut Topology of Artificial Neural Networks
In this paper, we investigate the expressivity and generalizability of a novel sparse shortcut topology.
Hybrid deep neural networks for all-cause Mortality Prediction from LDCT Images
In this paper, we propose a deep learning based method, which takes both chest LDCT image patches and coronary artery calcification risk scores as input, for direct prediction of mortality risk of lung cancer subjects.
Learning Deep Similarity Metric for 3D MR-TRUS Registration
Conclusion: A similarity metric that is learned using a deep neural network can be used to assess the quality of any given image registration and can be used in conjunction with the aforementioned optimization framework to perform automatic registration that is robust to poor initialization.
Adversarial Image Registration with Application for MR and TRUS Image Fusion
Robust and accurate alignment of multimodal medical images is a very challenging task, which however is very useful for many clinical applications.
Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss
In this paper, we introduce a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity.
Deeply-Supervised CNN for Prostate Segmentation
Prostate segmentation from Magnetic Resonance (MR) images plays an important role in image guided interven- tion.
CT Image Denoising with Perceptive Deep Neural Networks
Reduction of radiation dose associated with CT can increase noise and artifacts, which can adversely affect diagnostic confidence.
