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Found 9 papers, 2 papers with code
Paired Diffusion: Generation of related, synthetic PET-CT-Segmentation scans using Linked Denoising Diffusion Probabilistic Models
The rapid advancement of Artificial Intelligence (AI) in biomedical imaging and radiotherapy is hindered by the limited availability of large imaging data repositories.
Multi-Task Cooperative Learning via Searching for Flat Minima
In this work, we propose to formulate MTL as a multi/bi-level optimization problem, and therefore force features to learn from each task in a cooperative approach.
VertXNet: An Ensemble Method for Vertebrae Segmentation and Identification of Spinal X-Ray
Our experimental results have shown that the proposed pipeline outperformed two SOTA segmentation models on our test dataset (MEASURE 1) with a mean Dice of 0. 90, vs. a mean Dice of 0. 73 for Mask R-CNN and 0. 72 for U-Net.
VertXNet: Automatic Segmentation and Identification of Lumbar and Cervical Vertebrae from Spinal X-ray Images
Manual annotation of vertebrae on spinal X-ray imaging is costly and time-consuming due to bone shape complexity and image quality variations.
Recursive Deformable Image Registration Network with Mutual Attention
Deformable image registration, estimating the spatial transformation between different images, is an important task in medical imaging.
Ranked #3 on
Image Registration
on Unpaired-lung-CT
Residual Aligner Network
Image registration is important for medical imaging, the estimation of the spatial transformation between different images.
D-Net: Siamese based Network with Mutual Attention for Volume Alignment
Alignment of contrast and non-contrast-enhanced imaging is essential for the quantification of changes in several biomedical applications.
Automated fetal brain extraction from clinical Ultrasound volumes using 3D Convolutional Neural Networks
To improve the performance of most neuroimiage analysis pipelines, brain extraction is used as a fundamental first step in the image processing.
Pieces-of-parts for supervoxel segmentation with global context: Application to DCE-MRI tumour delineation
Rectal tumour segmentation in dynamic contrast-enhanced MRI (DCE-MRI) is a challenging task, and an automated and consistent method would be highly desirable to improve the modelling and prediction of patient outcomes from tissue contrast enhancement characteristics - particularly in routine clinical practice.
