The Journal of Machine Learning for Biomedical Imaging
Overview
The Journal of Machine Learning for Biomedical Imaging is a scientific journal, published since 2020 in English. The journal's country of origin is United States.
Details
Details
Abbr.
J Mach Learn Biomed Imaging
Start
2020
End
Continuing
Frequency
Irregular
e-ISSN
2766-905X
Country
United States
Language
English
Recent Articles
1.
Diaz I, Geiger M, McKinley R
J Mach Learn Biomed Imaging
. 2024 Dec;
2(May 2024):834-855.
PMID: 39655282
Convolutional neural networks (CNNs) allow for parameter sharing and translational equivariance by using convolutional kernels in their linear layers. By restricting these kernels to be SO(3)-steerable, CNNs can further improve...
2.
Abulnaga S, Dey N, Young S, Pan E, Hobgood K, Wang C, et al.
J Mach Learn Biomed Imaging
. 2024 Oct;
2(PIPPI 2022):527-546.
PMID: 39469044
Blood oxygen level dependent (BOLD) MRI time series with maternal hyperoxia can assess placental oxygenation and function. Measuring precise BOLD changes in the placenta requires accurate temporal placental segmentation and...
3.
Yao T, Qu C, Long J, Liu Q, Deng R, Tian Y, et al.
J Mach Learn Biomed Imaging
. 2023 Apr;
1.
PMID: 37077404
With the rapid development of self-supervised learning (e.g., contrastive learning), the importance of having large-scale images (even without annotations) for training a more generalizable AI model has been widely recognized...
4.
Mehta R, Filos A, Baid U, Sako C, McKinley R, Rebsamen M, et al.
J Mach Learn Biomed Imaging
. 2023 Mar;
2022.
PMID: 36998700
Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g.,...
5.
Yang C, Vemuri B
J Mach Learn Biomed Imaging
. 2023 Feb;
2022.
PMID: 36818740
In the recent past, nested structures in Riemannian manifolds has been studied in the context of dimensionality reduction as an alternative to the popular principal geodesic analysis (PGA) technique, for...
6.
Akrami H, Joshi A, Aydore S, Leahy R
J Mach Learn Biomed Imaging
. 2023 Jan;
1.
PMID: 36712144
Despite impressive state-of-the-art performance on a wide variety of machine learning tasks in multiple applications, deep learning methods can produce over-confident predictions, particularly with limited training data. Therefore, quantifying uncertainty...
7.
Singh N, Iglesias J, Adalsteinsson E, Dalca A, Golland P
J Mach Learn Biomed Imaging
. 2022 Nov;
2022.
PMID: 36349348
We propose neural network layers that explicitly combine frequency and image feature representations and show that they can be used as a versatile building block for reconstruction from frequency space...
8.
Hoopes A, Hoffmann M, Greve D, Fischl B, Guttag J, Dalca A
J Mach Learn Biomed Imaging
. 2022 Sep;
1.
PMID: 36147449
We introduce HyperMorph, a framework that facilitates efficient hyperparameter tuning in learning-based deformable image registration. Classical registration algorithms perform an iterative pair-wise optimization to compute a deformation field that aligns...
9.
Pal A, Rathi Y
J Mach Learn Biomed Imaging
. 2022 Jun;
1.
PMID: 35722657
Following the success of deep learning in a wide range of applications, neural network-based machine-learning techniques have received significant interest for accelerating magnetic resonance imaging (MRI) acquisition and reconstruction strategies....