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Found 16 papers, 3 papers with code
Prompt-driven Universal Model for View-Agnostic Echocardiography Analysis
Echocardiography segmentation for cardiac analysis is time-consuming and resource-intensive due to the variability in image quality and the necessity to process scans from various standard views.
Large-scale Graph Representation Learning of Dynamic Brain Connectome with Transformers
Here, we propose a method for learning the representation of dynamic functional connectivity with Graph Transformers.
MediViSTA-SAM: Zero-shot Medical Video Analysis with Spatio-temporal SAM Adaptation for Echocardiography
The Segmentation Anything Model (SAM) has gained significant attention for its robust generalization capabilities across diverse downstream tasks.
DULDA: Dual-domain Unsupervised Learned Descent Algorithm for PET image reconstruction
Deep learning based PET image reconstruction methods have achieved promising results recently.
A Noise-level-aware Framework for PET Image Denoising
Our hypothesis is that by explicitly providing the local relative noise level of the input image to a deep convolutional neural network (DCNN), the DCNN can outperform itself trained on image appearance only.
Federated Active Learning (F-AL): an Efficient Annotation Strategy for Federated Learning
Federated learning (FL) has been intensively investigated in terms of communication efficiency, privacy, and fairness.
Deep Metric Learning-based Image Retrieval System for Chest Radiograph and its Clinical Applications in COVID-19
These results demonstrate our deep metric learning based image retrieval model is highly efficient in the CXR retrieval, diagnosis and prognosis, and thus has great clinical value for the treatment and management of COVID-19 patients.
Deep Learning-based Four-region Lung Segmentation in Chest Radiography for COVID-19 Diagnosis
A segmentation model to separate left and right lung is firstly applied, and then a carina and left hilum detection network is used, which are the clinical landmarks to separate the upper and lower lungs.
Consensus Neural Network for Medical Imaging Denoising with Only Noisy Training Samples
In this paper we proposed a training method which learned denoising neural networks from noisy training samples only.
Computationally Efficient Deep Neural Network for Computed Tomography Image Reconstruction
The purpose of this work is to reduce the memory and time consumption of the training of the reconstruction networks for CT to make it practical for current hardware, while maintaining the quality of the reconstructed images.
Learning Personalized Representation for Inverse Problems in Medical Imaging Using Deep Neural Network
The representation is expressed using a deep neural network with the patient's prior images as network input.
End-to-End Abnormality Detection in Medical Imaging
To align the acquisition with the annotations made by radiologists in the image domain, a DNN was built as the unrolled version of iterative reconstruction algorithms to map the acquisitions to images, and followed by a 3D convolutional neural network (CNN) to detect the abnormality in the reconstructed images.
Attenuation correction for brain PET imaging using deep neural network based on dixon and ZTE MR images
With only Dixon MR images as the network input, the existing U-net structure was adopted and analysis using forty patient data sets shows it is superior than other Dixon based methods.
End-to-end Lung Nodule Detection in Computed Tomography
With 144 multi-slice fanbeam pro-jections, the proposed end-to-end detector could achieve comparable sensitivity with the reference detector, which was trained and applied on the fully-sampled image data.
Iterative PET Image Reconstruction Using Convolutional Neural Network Representation
An innovative feature of the proposed method is that we embed the neural network in the iterative reconstruction framework for image representation, rather than using it as a post-processing tool.
A Cascaded Convolutional Neural Network for X-ray Low-dose CT Image Denoising
Image denoising techniques are essential to reducing noise levels and enhancing diagnosis reliability in low-dose computed tomography (CT).
