Tootfinder

This https://arxiv.org/abs/2411.06842 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

DRIFTS: Optimizing Domain Randomization with Synthetic Data and Weight Interpolation for Fetal Brain Tissue Segmentation
Fetal brain tissue segmentation in magnetic resonance imaging (MRI) is a crucial tool that supports understanding of neurodevelopment, yet it faces challenges due to the heterogeneity of data coming from different scanners and settings, as well as data scarcity. Recent approaches based on domain randomization, like SynthSeg, have shown great potential for single-source domain generalization by simulating images with randomized contrast and image resolution from the label maps. In this work, we …

This https://arxiv.org/abs/2306.16297 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

A Meta-Learning Method for Estimation of Causal Excursion Effects to Assess Time-Varying Moderation
Advances in wearable technologies and health interventions delivered by smartphones have greatly increased the accessibility of mobile health (mHealth) interventions. Micro-randomized trials (MRTs) are designed to assess the effectiveness of the mHealth intervention and introduce a novel class of causal estimands called "causal excursion effects." These estimands enable the evaluation of how intervention effects change over time and are influenced by individual characteristics or context. Exist…

This https://arxiv.org/abs/2505.12494 has been replaced.
initial toot: https://mastoxiv.page/@arX…

SMURF: Scalable method for unsupervised reconstruction of flow in 4D flow MRI
We introduce SMURF, a scalable and unsupervised machine learning method for simultaneously segmenting vascular geometries and reconstructing velocity fields from 4D flow MRI data. SMURF models geometry and velocity fields using multilayer perceptron-based functions incorporating Fourier feature embeddings and random weight factorization to accelerate convergence. A measurement model connects these fields to the observed image magnitude and phase data. Maximum likelihood estimation and subsampli…