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Found 8 papers, 3 papers with code
Exploring the similarity of medical imaging classification problems
Supervised learning is ubiquitous in medical image analysis.
4D Semantic Cardiac Magnetic Resonance Image Synthesis on XCAT Anatomical Model
Using the parameterized motion model of the XCAT heart, we generate labels for 25 time frames of the heart for one cardiac cycle at 18 locations for the short axis view.
XCAT-GAN for Synthesizing 3D Consistent Labeled Cardiac MR Images on Anatomically Variable XCAT Phantoms
Moreover, the improvement in utilizing synthetic images for augmenting the real data is evident through the reduction of Hausdorff distance up to 28% and an increase in the Dice score up to 5%, indicating a higher similarity to the ground truth in all dimensions.
Hybrid Deep Neural Network for Brachial Plexus Nerve Segmentation in Ultrasound Images
In this paper, we propose a hybrid model consisting of a classification model followed by a segmentation model to segment BP nerve regions in ultrasound images.
PROMISSING: Pruning Missing Values in Neural Networks
In this method, there is no need to remove or impute the missing values; instead, the missing values are treated as a new source of information (representing what we do not know).
sim2real: Cardiac MR Image Simulation-to-Real Translation via Unsupervised GANs
There has been considerable interest in the MR physics-based simulation of a database of virtual cardiac MR images for the development of deep-learning analysis networks.
Pathology Synthesis of 3D-Consistent Cardiac MR Images using 2D VAEs and GANs
We devise three approaches for label manipulation in the latent space of the trained VAE model; i) \textbf{intra-subject synthesis} aiming to interpolate the intermediate slices of a subject to increase the through-plane resolution, ii) \textbf{inter-subject synthesis} aiming to interpolate the geometry and appearance of intermediate images between two dissimilar subjects acquired with different scanner vendors, and iii) \textbf{pathology synthesis} aiming to synthesize a series of pseudo-pathological synthetic subjects with characteristics of a desired heart disease.
Multi-head Attention-based Deep Multiple Instance Learning
This paper introduces MAD-MIL, a Multi-head Attention-based Deep Multiple Instance Learning model, designed for weakly supervised Whole Slide Images (WSIs) classification in digital pathology.
