Search Results for author:
Found 12 papers, 2 papers with code
Shape-aware Segmentation of the Placenta in BOLD Fetal MRI Time Series
In this work, we propose a machine learning segmentation framework for placental BOLD MRI and apply it to segmenting each volume in a time series.
Quantifying white matter hyperintensity and brain volumes in heterogeneous clinical and low-field portable MRI
Brain atrophy and white matter hyperintensity (WMH) are critical neuroimaging features for ascertaining brain injury in cerebrovascular disease and multiple sclerosis.
Fully Convolutional Slice-to-Volume Reconstruction for Single-Stack MRI
Here, we propose a SVR method that overcomes the shortcomings of previous work and produces state-of-the-art reconstructions in the presence of extreme inter-slice motion.
A Framework for Interpretability in Machine Learning for Medical Imaging
To answer these questions, we identify a need to formalize the goals and elements of interpretability in MLMI.
Diffeomorphic Multi-Resolution Deep Learning Registration for Applications in Breast MRI
In breast surgical planning, accurate registration of MR images across patient positions has the potential to improve the localisation of tumours during breast cancer treatment.
SUD$^2$: Supervision by Denoising Diffusion Models for Image Reconstruction
Many imaging inverse problems$\unicode{x2014}$such as image-dependent in-painting and dehazing$\unicode{x2014}$are challenging because their forward models are unknown or depend on unknown latent parameters.
Automatic Segmentation of the Placenta in BOLD MRI Time Series
In this work, we propose a machine learning model based on a U-Net neural network architecture to automatically segment the placenta in BOLD MRI and apply it to segmenting each volume in a time series.
SuperWarp: Supervised Learning and Warping on U-Net for Invariant Subvoxel-Precise Registration
In recent years, learning-based image registration methods have gradually moved away from direct supervision with target warps to instead use self-supervision, with excellent results in several registration benchmarks.
Supervision by Denoising for Medical Image Segmentation
SUD unifies stochastic averaging and spatial denoising techniques under a spatio-temporal denoising framework and alternates denoising and model weight update steps in an optimization framework for semi-supervision.
Transform Quantization for CNN (Convolutional Neural Network) Compression
In this paper, we compress convolutional neural network (CNN) weights post-training via transform quantization.
Non-Line-of-Sight Surface Reconstruction Using the Directional Light-Cone Transform
We propose a joint albedo-normal approach to non-line-of-sight (NLOS) surface reconstruction using the directional light-cone transform (D-LCT).
Solving Vision Problems via Filtering
We propose a new, filtering approach for solving a large number of regularized inverse problems commonly found in computer vision.
