33 research outputs found
Self-Supervised MRI Reconstruction with Unrolled Diffusion Models
Magnetic Resonance Imaging (MRI) produces excellent soft tissue contrast,
albeit it is an inherently slow imaging modality. Promising deep learning
methods have recently been proposed to reconstruct accelerated MRI scans.
However, existing methods still suffer from various limitations regarding image
fidelity, contextual sensitivity, and reliance on fully-sampled acquisitions
for model training. To comprehensively address these limitations, we propose a
novel self-supervised deep reconstruction model, named Self-Supervised
Diffusion Reconstruction (SSDiffRecon). SSDiffRecon expresses a conditional
diffusion process as an unrolled architecture that interleaves cross-attention
transformers for reverse diffusion steps with data-consistency blocks for
physics-driven processing. Unlike recent diffusion methods for MRI
reconstruction, a self-supervision strategy is adopted to train SSDiffRecon
using only undersampled k-space data. Comprehensive experiments on public brain
MR datasets demonstrates the superiority of SSDiffRecon against
state-of-the-art supervised, and self-supervised baselines in terms of
reconstruction speed and quality. Implementation will be available at
https://github.com/yilmazkorkmaz1/SSDiffRecon
Interpretable Medical Image Classification using Prototype Learning and Privileged Information
Interpretability is often an essential requirement in medical imaging.
Advanced deep learning methods are required to address this need for
explainability and high performance. In this work, we investigate whether
additional information available during the training process can be used to
create an understandable and powerful model. We propose an innovative solution
called Proto-Caps that leverages the benefits of capsule networks, prototype
learning and the use of privileged information. Evaluating the proposed
solution on the LIDC-IDRI dataset shows that it combines increased
interpretability with above state-of-the-art prediction performance. Compared
to the explainable baseline model, our method achieves more than 6 % higher
accuracy in predicting both malignancy (93.0 %) and mean characteristic
features of lung nodules. Simultaneously, the model provides case-based
reasoning with prototype representations that allow visual validation of
radiologist-defined attributes.Comment: MICCAI 2023 Medical Image Computing and Computer Assisted
Interventio
FedAutoMRI: Federated Neural Architecture Search for MR Image Reconstruction
Centralized training methods have shown promising results in MR image
reconstruction, but privacy concerns arise when gathering data from multiple
institutions. Federated learning, a distributed collaborative training scheme,
can utilize multi-center data without the need to transfer data between
institutions. However, existing federated learning MR image reconstruction
methods rely on manually designed models which have extensive parameters and
suffer from performance degradation when facing heterogeneous data
distributions. To this end, this paper proposes a novel FederAted neUral
archiTecture search approach fOr MR Image reconstruction (FedAutoMRI). The
proposed method utilizes differentiable architecture search to automatically
find the optimal network architecture. In addition, an exponential moving
average method is introduced to improve the robustness of the client model to
address the data heterogeneity issue. To the best of our knowledge, this is the
first work to use federated neural architecture search for MR image
reconstruction. Experimental results demonstrate that our proposed FedAutoMRI
can achieve promising performances while utilizing a lightweight model with
only a small number of model parameters compared to the classical federated
learning methods.Comment: 10 page
Context-Aware Pseudo-Label Refinement for Source-Free Domain Adaptive Fundus Image Segmentation
In the domain adaptation problem, source data may be unavailable to the
target client side due to privacy or intellectual property issues. Source-free
unsupervised domain adaptation (SF-UDA) aims at adapting a model trained on the
source side to align the target distribution with only the source model and
unlabeled target data. The source model usually produces noisy and
context-inconsistent pseudo-labels on the target domain, i.e., neighbouring
regions that have a similar visual appearance are annotated with different
pseudo-labels. This observation motivates us to refine pseudo-labels with
context relations. Another observation is that features of the same class tend
to form a cluster despite the domain gap, which implies context relations can
be readily calculated from feature distances. To this end, we propose a
context-aware pseudo-label refinement method for SF-UDA. Specifically, a
context-similarity learning module is developed to learn context relations.
Next, pseudo-label revision is designed utilizing the learned context
relations. Further, we propose calibrating the revised pseudo-labels to
compensate for wrong revision caused by inaccurate context relations.
Additionally, we adopt a pixel-level and class-level denoising scheme to select
reliable pseudo-labels for domain adaptation. Experiments on cross-domain
fundus images indicate that our approach yields the state-of-the-art results.
Code is available at https://github.com/xmed-lab/CPR.Comment: Accepted by MICCAI 2023, 11 page
The Significance of Machine Learning in Clinical Disease Diagnosis: A Review
The global need for effective disease diagnosis remains substantial, given
the complexities of various disease mechanisms and diverse patient symptoms. To
tackle these challenges, researchers, physicians, and patients are turning to
machine learning (ML), an artificial intelligence (AI) discipline, to develop
solutions. By leveraging sophisticated ML and AI methods, healthcare
stakeholders gain enhanced diagnostic and treatment capabilities. However,
there is a scarcity of research focused on ML algorithms for enhancing the
accuracy and computational efficiency. This research investigates the capacity
of machine learning algorithms to improve the transmission of heart rate data
in time series healthcare metrics, concentrating particularly on optimizing
accuracy and efficiency. By exploring various ML algorithms used in healthcare
applications, the review presents the latest trends and approaches in ML-based
disease diagnosis (MLBDD). The factors under consideration include the
algorithm utilized, the types of diseases targeted, the data types employed,
the applications, and the evaluation metrics. This review aims to shed light on
the prospects of ML in healthcare, particularly in disease diagnosis. By
analyzing the current literature, the study provides insights into
state-of-the-art methodologies and their performance metrics.Comment: 8 page
Diffusion-Model-Assisted Supervised Learning of Generative Models for Density Estimation
We present a supervised learning framework of training generative models for
density estimation. Generative models, including generative adversarial
networks, normalizing flows, variational auto-encoders, are usually considered
as unsupervised learning models, because labeled data are usually unavailable
for training. Despite the success of the generative models, there are several
issues with the unsupervised training, e.g., requirement of reversible
architectures, vanishing gradients, and training instability. To enable
supervised learning in generative models, we utilize the score-based diffusion
model to generate labeled data. Unlike existing diffusion models that train
neural networks to learn the score function, we develop a training-free score
estimation method. This approach uses mini-batch-based Monte Carlo estimators
to directly approximate the score function at any spatial-temporal location in
solving an ordinary differential equation (ODE), corresponding to the
reverse-time stochastic differential equation (SDE). This approach can offer
both high accuracy and substantial time savings in neural network training.
Once the labeled data are generated, we can train a simple fully connected
neural network to learn the generative model in the supervised manner. Compared
with existing normalizing flow models, our method does not require to use
reversible neural networks and avoids the computation of the Jacobian matrix.
Compared with existing diffusion models, our method does not need to solve the
reverse-time SDE to generate new samples. As a result, the sampling efficiency
is significantly improved. We demonstrate the performance of our method by
applying it to a set of 2D datasets as well as real data from the UCI
repository
AnoDODE: Anomaly Detection with Diffusion ODE
Anomaly detection is the process of identifying atypical data samples that
significantly deviate from the majority of the dataset. In the realm of
clinical screening and diagnosis, detecting abnormalities in medical images
holds great importance. Typically, clinical practice provides access to a vast
collection of normal images, while abnormal images are relatively scarce. We
hypothesize that abnormal images and their associated features tend to manifest
in low-density regions of the data distribution. Following this assumption, we
turn to diffusion ODEs for unsupervised anomaly detection, given their
tractability and superior performance in density estimation tasks. More
precisely, we propose a new anomaly detection method based on diffusion ODEs by
estimating the density of features extracted from multi-scale medical images.
Our anomaly scoring mechanism depends on computing the negative log-likelihood
of features extracted from medical images at different scales, quantified in
bits per dimension. Furthermore, we propose a reconstruction-based anomaly
localization suitable for our method. Our proposed method not only identifie
anomalies but also provides interpretability at both the image and pixel
levels. Through experiments on the BraTS2021 medical dataset, our proposed
method outperforms existing methods. These results confirm the effectiveness
and robustness of our method.Comment: 11 pages, 5 figure