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Found 10 papers, 1 papers with code
Nucleus subtype classification using inter-modality learning
In this paper, we propose to use inter-modality learning to label previously un-labelable cell types on virtual H&E.
Super-Resolution Multi-Contrast Unbiased Eye Atlases With Deep Probabilistic Refinement
These variations limit the feasibility and robustness of generalizing population-wise features of eye organs to an unbiased spatial reference.
AniRes2D: Anisotropic Residual-enhanced Diffusion for 2D MR Super-Resolution
Our findings contribute valuable insights to the application of DDPMs for SR of anisotropic MR images.
Towards an accurate and generalizable multiple sclerosis lesion segmentation model using self-ensembled lesion fusion
Automatic multiple sclerosis (MS) lesion segmentation using multi-contrast magnetic resonance (MR) images provides improved efficiency and reproducibility compared to manual delineation.
Harmonization-enriched domain adaptation with light fine-tuning for multiple sclerosis lesion segmentation
Our experiments illustrate that the amalgamation of one-shot adaptation data with harmonized training data surpasses the performance of utilizing either data source in isolation.
Exploring shared memory architectures for end-to-end gigapixel deep learning
The M1 Ultra SoC was able to train the model directly on gigapixel images (16000$\times$64000 pixels, 1. 024 billion pixels) with a batch size of 1 using over 100 GB of unified memory for the process at an average speed of 1 minute and 21 seconds per batch with Tensorflow 2/Keras.
HACA3: A Unified Approach for Multi-site MR Image Harmonization
Furthermore, HACA3 is also robust to imaging artifacts and can be trained and applied to any set of MR contrasts.
Deep filter bank regression for super-resolution of anisotropic MR brain images
In 2D multi-slice magnetic resonance (MR) acquisition, the through-plane signals are typically of lower resolution than the in-plane signals.
Extracting 2D weak labels from volume labels using multiple instance learning in CT hemorrhage detection
Multiple instance learning (MIL) is a supervised learning methodology that aims to allow models to learn instance class labels from bag class labels, where a bag is defined to contain multiple instances.
Generalizing Deep Whole Brain Segmentation for Pediatric and Post-Contrast MRI with Augmented Transfer Learning
Generalizability is an important problem in deep neural networks, especially in the context of the variability of data acquisition in clinical magnetic resonance imaging (MRI).
