12 research outputs found
MAEEG: Masked Auto-encoder for EEG Representation Learning
Decoding information from bio-signals such as EEG, using machine learning has
been a challenge due to the small data-sets and difficulty to obtain labels. We
propose a reconstruction-based self-supervised learning model, the masked
auto-encoder for EEG (MAEEG), for learning EEG representations by learning to
reconstruct the masked EEG features using a transformer architecture. We found
that MAEEG can learn representations that significantly improve sleep stage
classification (~5% accuracy increase) when only a small number of labels are
given. We also found that input sample lengths and different ways of masking
during reconstruction-based SSL pretraining have a huge effect on downstream
model performance. Specifically, learning to reconstruct a larger proportion
and more concentrated masked signal results in better performance on sleep
classification. Our findings provide insight into how reconstruction-based SSL
could help representation learning for EEG.Comment: 10 pages, 5 figures, accepted by Workshop on Learning from Time
Series for Health, NeurIPS2022 as poster presentatio
Noise2Recon: Enabling Joint MRI Reconstruction and Denoising with Semi-Supervised and Self-Supervised Learning
Deep learning (DL) has shown promise for faster, high quality accelerated MRI
reconstruction. However, supervised DL methods depend on extensive amounts of
fully-sampled (labeled) data and are sensitive to out-of-distribution (OOD)
shifts, particularly low signal-to-noise ratio (SNR) acquisitions. To alleviate
this challenge, we propose Noise2Recon, a model-agnostic, consistency training
method for joint MRI reconstruction and denoising that can use both
fully-sampled (labeled) and undersampled (unlabeled) scans in semi-supervised
and self-supervised settings. With limited or no labeled training data,
Noise2Recon outperforms compressed sensing and deep learning baselines,
including supervised networks, augmentation-based training, fine-tuned
denoisers, and self-supervised methods, and matches performance of supervised
models, which were trained with 14x more fully-sampled scans. Noise2Recon also
outperforms all baselines, including state-of-the-art fine-tuning and
augmentation techniques, among low-SNR scans and when generalizing to other OOD
factors, such as changes in acceleration factors and different datasets.
Augmentation extent and loss weighting hyperparameters had negligible impact on
Noise2Recon compared to supervised methods, which may indicate increased
training stability. Our code is available at https://github.com/ad12/meddlr
Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and Challenges
Magnetic Resonance Imaging (MRI) suffers from several artifacts, the most
common of which are motion artifacts. These artifacts often yield images that
are of non-diagnostic quality. To detect such artifacts, images are
prospectively evaluated by experts for their diagnostic quality, which
necessitates patient-revisits and rescans whenever non-diagnostic quality scans
are encountered. This motivates the need to develop an automated framework
capable of accessing medical image quality and detecting diagnostic and
non-diagnostic images. In this paper, we explore several convolutional neural
network-based frameworks for medical image quality assessment and investigate
several challenges therein.Comment: 4 pages, 8 Figures, Conference Submissio
Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and Challenges
Magnetic Resonance Imaging (MRI) suffers from several artifacts, the most common of which are motion artifacts. These artifacts often yield images that are of non-diagnostic quality. To detect such artifacts, images are prospectively evaluated by experts for their diagnostic quality, which necessitates patient-revisits and rescans whenever non-diagnostic quality scans are encountered. This motivates the need to develop an automated framework capable of accessing medical image quality and detecting diagnostic and non-diagnostic images. In this paper, we explore several convolutional neural network-based frameworks for medical image quality assessment and investigate several challenges therein
Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and Challenges
Magnetic Resonance Imaging (MRI) suffers from several artifacts, the most common of which are motion artifacts. These artifacts often yield images that are of non-diagnostic quality. To detect such artifacts, images are prospectively evaluated by experts for their diagnostic quality, which necessitates patient-revisits and rescans whenever non-diagnostic quality scans are encountered. This motivates the need to develop an automated framework capable of accessing medical image quality and detecting diagnostic and non-diagnostic images. In this paper, we explore several convolutional neural network-based frameworks for medical image quality assessment and investigate several challenges therein
Coil Sketching for computationally-efficient MR iterative reconstruction
Purpose: Parallel imaging and compressed sensing reconstructions of large MRI
datasets often have a prohibitive computational cost that bottlenecks clinical
deployment, especially for 3D non-Cartesian acquisitions. One common approach
is to reduce the number of coil channels actively used during reconstruction as
in coil compression. While effective for Cartesian imaging, coil compression
inherently loses signal energy, producing shading artifacts that compromise
image quality for 3D non-Cartesian imaging. We propose coil sketching, a
general and versatile method for computationally-efficient iterative MR image
reconstruction.
Theory and Methods: We based our method on randomized sketching algorithms, a
type of large-scale optimization algorithms well established in the fields of
machine learning and big data analysis. We adapt the sketching theory to the
MRI reconstruction problem via a structured sketching matrix that, similar to
coil compression, reduces the number of coils concurrently used during
reconstruction, but unlike coil compression, is able to leverage energy from
all coils.
Results: First, we performed ablation experiments to validate the sketching
matrix design on both Cartesian and non-Cartesian datasets. The resulting
design yielded both improved computational efficiency and preserved
signal-to-noise ratio (SNR) as measured by the inverse g-factor. Then, we
verified the efficacy of our approach on high-dimensional non-Cartesian 3D
cones datasets, where coil sketching yielded up to three-fold faster
reconstructions with equivalent image quality.
Conclusion: Coil sketching is a general and versatile reconstruction
framework for computationally fast and memory-efficient reconstruction.Comment: 16 pages, 7 figures, 2 table