331 research outputs found
Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging
Many analyses of neuroimaging data involve studying one or more regions of
interest (ROIs) in a brain image. In order to do so, each ROI must first be
identified. Since every brain is unique, the location, size, and shape of each
ROI varies across subjects. Thus, each ROI in a brain image must either be
manually identified or (semi-) automatically delineated, a task referred to as
segmentation. Automatic segmentation often involves mapping a previously
manually segmented image to a new brain image and propagating the labels to
obtain an estimate of where each ROI is located in the new image. A more recent
approach to this problem is to propagate labels from multiple manually
segmented atlases and combine the results using a process known as label
fusion. To date, most label fusion algorithms either employ voting procedures
or impose prior structure and subsequently find the maximum a posteriori
estimator (i.e., the posterior mode) through optimization. We propose using a
fully Bayesian spatial regression model for label fusion that facilitates
direct incorporation of covariate information while making accessible the
entire posterior distribution. We discuss the implementation of our model via
Markov chain Monte Carlo and illustrate the procedure through both simulation
and application to segmentation of the hippocampus, an anatomical structure
known to be associated with Alzheimer's disease.Comment: 24 pages, 10 figure
Intelligent Painter: Picture Composition With Resampling Diffusion Model
Have you ever thought that you can be an intelligent painter? This means that
you can paint a picture with a few expected objects in mind, or with a
desirable scene. This is different from normal inpainting approaches for which
the location of specific objects cannot be determined. In this paper, we
present an intelligent painter that generate a person's imaginary scene in one
go, given explicit hints. We propose a resampling strategy for Denoising
Diffusion Probabilistic Model (DDPM) to intelligently compose unconditional
harmonized pictures according to the input subjects at specific locations. By
exploiting the diffusion property, we resample efficiently to produce realistic
pictures. Experimental results show that our resampling method favors the
semantic meaning of the generated output efficiently and generates less blurry
output. Quantitative analysis of image quality assessment shows that our method
produces higher perceptual quality images compared with the state-of-the-art
methods.Comment: ICIP 202
Unsupervised deep learning research and implementation of variational autoencoders
Generative models have been one of the major research fields in unsupervised deep
learning during the last years. They are achieving promising results in learning the distribution
of multidimensional variables as well as in finding meaningful hidden representations
in data.
The aim of this thesis is to gain a sound understanding of generative models through a
profound study of one of the most promising and widely used generative models family,
the variational autoencoders. In particular, the performance of the standard variational
autoencoder (known as VAE) and the Gaussian Mixture variational autoencoder (called
GMVAE) is assessed. First, the mathematical and probabilistic basis of both models is
presented. Then, the models are implemented in Python using the Tensorflow framework.
The source code is freely available and documented in a personal GitHub repository created
for this thesis. Later, the performance of the implemented models is appraised in
terms of generative capabilities and interpretability of the hidden representation of the
inputs. Two real datasets are used during the experiments, the MNIST and "Frey faces".
Results show the models implemented work correctly, and they also show the GMVAE
outweighs the performance of the standard VAE, as expected.IngenierÃa en TecnologÃas de Telecomunicació
Artificial intelligence for dementia prevention
INTRODUCTION:
A wide range of modifiable risk factors for dementia have been identified. Considerable debate remains about these risk factors, possible interactions between them or with genetic risk, and causality, and how they can help in clinical trial recruitment and drug development. Artificial intelligence (AI) and machine learning (ML) may refine understanding.//
METHODS:
ML approaches are being developed in dementia prevention. We discuss exemplar uses and evaluate the current applications and limitations in the dementia prevention field.//
RESULTS:
Risk-profiling tools may help identify high-risk populations for clinical trials; however, their performance needs improvement. New risk-profiling and trial-recruitment tools underpinned by ML models may be effective in reducing costs and improving future trials. ML can inform drug-repurposing efforts and prioritization of disease-modifying therapeutics.//
DISCUSSION:
ML is not yet widely used but has considerable potential to enhance precision in dementia prevention
SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-training
In an era where symbolic mathematical equations are indispensable for
modeling complex natural phenomena, scientific inquiry often involves
collecting observations and translating them into mathematical expressions.
Recently, deep learning has emerged as a powerful tool for extracting insights
from data. However, existing models typically specialize in either numeric or
symbolic domains, and are usually trained in a supervised manner tailored to
specific tasks. This approach neglects the substantial benefits that could
arise from a task-agnostic unified understanding between symbolic equations and
their numeric counterparts. To bridge the gap, we introduce SNIP, a
Symbolic-Numeric Integrated Pre-training, which employs joint contrastive
learning between symbolic and numeric domains, enhancing their mutual
similarities in the pre-trained embeddings. By performing latent space
analysis, we observe that SNIP provides cross-domain insights into the
representations, revealing that symbolic supervision enhances the embeddings of
numeric data and vice versa. We evaluate SNIP across diverse tasks, including
symbolic-to-numeric mathematical property prediction and numeric-to-symbolic
equation discovery, commonly known as symbolic regression. Results show that
SNIP effectively transfers to various tasks, consistently outperforming fully
supervised baselines and competing strongly with established task-specific
methods, especially in few-shot learning scenarios where available data is
limited
Zero-shot Medical Image Translation via Frequency-Guided Diffusion Models
Recently, the diffusion model has emerged as a superior generative model that
can produce high quality and realistic images. However, for medical image
translation, the existing diffusion models are deficient in accurately
retaining structural information since the structure details of source domain
images are lost during the forward diffusion process and cannot be fully
recovered through learned reverse diffusion, while the integrity of anatomical
structures is extremely important in medical images. For instance, errors in
image translation may distort, shift, or even remove structures and tumors,
leading to incorrect diagnosis and inadequate treatments. Training and
conditioning diffusion models using paired source and target images with
matching anatomy can help. However, such paired data are very difficult and
costly to obtain, and may also reduce the robustness of the developed model to
out-of-distribution testing data. We propose a frequency-guided diffusion model
(FGDM) that employs frequency-domain filters to guide the diffusion model for
structure-preserving image translation. Based on its design, FGDM allows
zero-shot learning, as it can be trained solely on the data from the target
domain, and used directly for source-to-target domain translation without any
exposure to the source-domain data during training. We evaluated it on three
cone-beam CT (CBCT)-to-CT translation tasks for different anatomical sites, and
a cross-institutional MR imaging translation task. FGDM outperformed the
state-of-the-art methods (GAN-based, VAE-based, and diffusion-based) in metrics
of Frechet Inception Distance (FID), Peak Signal-to-Noise Ratio (PSNR), and
Structural Similarity Index Measure (SSIM), showing its significant advantages
in zero-shot medical image translation
Domain Generalization for Medical Image Analysis: A Survey
Medical Image Analysis (MedIA) has become an essential tool in medicine and
healthcare, aiding in disease diagnosis, prognosis, and treatment planning, and
recent successes in deep learning (DL) have made significant contributions to
its advances. However, DL models for MedIA remain challenging to deploy in
real-world situations, failing for generalization under the distributional gap
between training and testing samples, known as a distribution shift problem.
Researchers have dedicated their efforts to developing various DL methods to
adapt and perform robustly on unknown and out-of-distribution data
distributions. This paper comprehensively reviews domain generalization studies
specifically tailored for MedIA. We provide a holistic view of how domain
generalization techniques interact within the broader MedIA system, going
beyond methodologies to consider the operational implications on the entire
MedIA workflow. Specifically, we categorize domain generalization methods into
data-level, feature-level, model-level, and analysis-level methods. We show how
those methods can be used in various stages of the MedIA workflow with DL
equipped from data acquisition to model prediction and analysis. Furthermore,
we include benchmark datasets and applications used to evaluate these
approaches and analyze the strengths and weaknesses of various methods,
unveiling future research opportunities
A unified formal framework for factorial and probabilistic topic modelling
Topic modelling has become a highly popular technique for extracting knowledge from texts. It encompasses various method families, including Factorial methods, Probabilistic methods, and Natural Language Processing methods. This paper introduces a unified conceptual framework for Factorial and Probabilistic methods by identifying shared elements and representing them using a homogeneous notation. The paper presents 12 different methods within this framework, enabling easy comparative analysis to assess the flexibility and how realistic the assumptions of each approach are. This establishes the initial stage of a broader analysis aimed at relating all method families to this common framework, comprehensively understanding their strengths and weaknesses, and establishing general application guidelines. Also, an experimental setup reinforces the convenience of having harmonized notational schema. The paper concludes with a discussion on the presented methods and outlines future research directions.Peer ReviewedPostprint (published version
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