1 citations · 1 across the 6 of their papers we have counts for
6 papers
Predicting Human Brain States with Transformer
Yifei Sun, Mariano Cabezas, Jiah Lee +4
The human brain is a complex and highly dynamic system, and our current knowledge of its functional mechanism is still very limited. Fortunately, with functional magnetic resonance…
A benchmark for 2D foetal brain ultrasound analysis
Mariano Cabezas, Yago Diez, Clara Martinez-Diago +1
Brain development involves a sequence of structural changes from early stages of the embryo until several months after birth. Currently, ultrasound is the established technique for…
How Much Data are Enough? Investigating Dataset Requirements for Patch-Based Brain MRI Segmentation Tasks
Dongang Wang, Peilin Liu, Hengrui Wang +11
Training deep neural networks reliably requires access to large-scale datasets. However, obtaining such datasets can be challenging, especially in the context of neuroimaging analy…
Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Training
Lei Bai, Dongang Wang, Michael Barnett +13
Accurately measuring the evolution of Multiple Sclerosis (MS) with magnetic resonance imaging (MRI) critically informs understanding of disease progression and helps to direct ther…
Precise Few-shot Fat-free Thigh Muscle Segmentation in T1-weighted MRI
Sheng Chen, Zihao Tang, Dongnan Liu +5
Precise thigh muscle volumes are crucial to monitor the motor functionality of patients with diseases that may result in various degrees of thigh muscle loss. T1-weighted MRI is th…
Learning from pseudo-labels: deep networks improve consistency in longitudinal brain volume estimation
Geng Zhan, Dongang Wang, Mariano Cabezas +5
Brain atrophy is an important biomarker for monitoring neurodegeneration and disease progression in conditions such as multiple sclerosis (MS). An accurate and robust quantitative…