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Found 13 papers, 3 papers with code
Automated Classification of Body MRI Sequence Type Using Convolutional Neural Networks
In order to reduce clinician oversight and ensure the validity of the DICOM headers, we propose an automated method to classify the 3D MRI sequence acquired at the levels of the chest, abdomen, and pelvis.
Semantic Image Synthesis for Abdominal CT
As a new emerging and promising type of generative models, diffusion models have proven to outperform Generative Adversarial Networks (GANs) in multiple tasks, including image synthesis.
C-DARL: Contrastive diffusion adversarial representation learning for label-free blood vessel segmentation
Blood vessel segmentation in medical imaging is one of the essential steps for vascular disease diagnosis and interventional planning in a broad spectrum of clinical scenarios in image-based medicine and interventional medicine.
Diffusion Adversarial Representation Learning for Self-supervised Vessel Segmentation
Also, by adversarial learning based on the proposed switchable spatially-adaptive denormalization, our model estimates synthetic fake vessel images as well as vessel segmentation masks, which further makes the model capture vessel-relevant semantic information.
Diffusion Deformable Model for 4D Temporal Medical Image Generation
Our proposed DDM is composed of the diffusion and the deformation modules so that DDM can learn spatial deformation information between the source and target volumes and provide a latent code for generating intermediate frames along a geodesic path.
DiffuseMorph: Unsupervised Deformable Image Registration Using Diffusion Model
Specifically, the deformation fields are generated by the conditional score function of the deformation between the moving and fixed images, so that the registration can be performed from continuous deformation by simply scaling the latent feature of the score.
Federated Split Task-Agnostic Vision Transformer for COVID-19 CXR Diagnosis
For example, this enables neural network training for COVID-19 diagnosis on chest X-ray (CXR) images without collecting patient CXR data across multiple hospitals.
Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Training
For example, this enables neural network training for COVID-19 diagnosis on chest X-ray (CXR) images without collecting patient CXR data across multiple hospitals.
Privacy-preserving Task-Agnostic Vision Transformer for Image Processing
Inspired by the recent success of Vision Transformer (ViT), here we present a new distributed learning framework for image processing applications, allowing clients to learn multiple tasks with their private data.
Federated Split Task-Agnostic Vision Transformer for COVID-19 CXR Diagnosis
For example, this enables neural network training for COVID-19 diagnosis on chest X-ray (CXR) images without collecting patient CXR data across multiple hospitals.
CycleMorph: Cycle Consistent Unsupervised Deformable Image Registration
However, the existing deep learning methods still have limitation in the preservation of original topology during the deformation with registration vector fields.
Unsupervised Deformable Image Registration Using Cycle-Consistent CNN
Medical image registration is one of the key processing steps for biomedical image analysis such as cancer diagnosis.
Mumford-Shah Loss Functional for Image Segmentation with Deep Learning
This loss function is based on the observation that the softmax layer of deep neural networks has striking similarity to the characteristic function in the Mumford-Shah functional.
