Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Model Adaptation for Image Reconstruction using Generalized Stein's
  Unbiased Risk Estimator   Deep learning image reconstruction algorithms often suffer from model
mismatches when the acquisition scheme differs significantly from the forward
model used during training. We introduce a Generalized Stein's Unbiased Risk
Estimate (GSURE) loss metric to adapt the network to the measured k-space data
and minimize model misfit impact. Unlike current methods that rely on the mean
square error in kspace, the proposed metric accounts for noise in the
measurements. This makes the approach less vulnerable to overfitting, thus
offering improved reconstruction quality compared to schemes that rely on
mean-square error. This approach may be useful to rapidly adapt pre-trained
models to new acquisition settings (e.g., multi-site) and different contrasts
than training data

17
The categories of this paper are: q-fin
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an