472 research outputs found
Learning Discriminative Bayesian Networks from High-dimensional Continuous Neuroimaging Data
Due to its causal semantics, Bayesian networks (BN) have been widely employed
to discover the underlying data relationship in exploratory studies, such as
brain research. Despite its success in modeling the probability distribution of
variables, BN is naturally a generative model, which is not necessarily
discriminative. This may cause the ignorance of subtle but critical network
changes that are of investigation values across populations. In this paper, we
propose to improve the discriminative power of BN models for continuous
variables from two different perspectives. This brings two general
discriminative learning frameworks for Gaussian Bayesian networks (GBN). In the
first framework, we employ Fisher kernel to bridge the generative models of GBN
and the discriminative classifiers of SVMs, and convert the GBN parameter
learning to Fisher kernel learning via minimizing a generalization error bound
of SVMs. In the second framework, we employ the max-margin criterion and build
it directly upon GBN models to explicitly optimize the classification
performance of the GBNs. The advantages and disadvantages of the two frameworks
are discussed and experimentally compared. Both of them demonstrate strong
power in learning discriminative parameters of GBNs for neuroimaging based
brain network analysis, as well as maintaining reasonable representation
capacity. The contributions of this paper also include a new Directed Acyclic
Graph (DAG) constraint with theoretical guarantee to ensure the graph validity
of GBN.Comment: 16 pages and 5 figures for the article (excluding appendix
FRNET: Flattened Residual Network for Infant MRI Skull Stripping
Skull stripping for brain MR images is a basic segmentation task. Although
many methods have been proposed, most of them focused mainly on the adult MR
images. Skull stripping for infant MR images is more challenging due to the
small size and dynamic intensity changes of brain tissues during the early
ages. In this paper, we propose a novel CNN based framework to robustly extract
brain region from infant MR image without any human assistance. Specifically,
we propose a simplified but more robust flattened residual network architecture
(FRnet). We also introduce a new boundary loss function to highlight ambiguous
and low contrast regions between brain and non-brain regions. To make the whole
framework more robust to MR images with different imaging quality, we further
introduce an artifact simulator for data augmentation. We have trained and
tested our proposed framework on a large dataset (N=343), covering newborns to
48-month-olds, and obtained performance better than the state-of-the-art
methods in all age groups.Comment: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI
Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation
Semantic segmentation is essentially important to biomedical image analysis.
Many recent works mainly focus on integrating the Fully Convolutional Network
(FCN) architecture with sophisticated convolution implementation and deep
supervision. In this paper, we propose to decompose the single segmentation
task into three subsequent sub-tasks, including (1) pixel-wise image
segmentation, (2) prediction of the class labels of the objects within the
image, and (3) classification of the scene the image belonging to. While these
three sub-tasks are trained to optimize their individual loss functions of
different perceptual levels, we propose to let them interact by the task-task
context ensemble. Moreover, we propose a novel sync-regularization to penalize
the deviation between the outputs of the pixel-wise segmentation and the class
prediction tasks. These effective regularizations help FCN utilize context
information comprehensively and attain accurate semantic segmentation, even
though the number of the images for training may be limited in many biomedical
applications. We have successfully applied our framework to three diverse 2D/3D
medical image datasets, including Robotic Scene Segmentation Challenge 18
(ROBOT18), Brain Tumor Segmentation Challenge 18 (BRATS18), and Retinal Fundus
Glaucoma Challenge (REFUGE18). We have achieved top-tier performance in all
three challenges.Comment: IEEE Transactions on Medical Imagin
ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using Large Language Models
Large language models (LLMs) have recently demonstrated their potential in
clinical applications, providing valuable medical knowledge and advice. For
example, a large dialog LLM like ChatGPT has successfully passed part of the US
medical licensing exam. However, LLMs currently have difficulty processing
images, making it challenging to interpret information from medical images,
which are rich in information that supports clinical decisions. On the other
hand, computer-aided diagnosis (CAD) networks for medical images have seen
significant success in the medical field by using advanced deep-learning
algorithms to support clinical decision-making. This paper presents a method
for integrating LLMs into medical-image CAD networks. The proposed framework
uses LLMs to enhance the output of multiple CAD networks, such as diagnosis
networks, lesion segmentation networks, and report generation networks, by
summarizing and reorganizing the information presented in natural language text
format. The goal is to merge the strengths of LLMs' medical domain knowledge
and logical reasoning with the vision understanding capability of existing
medical-image CAD models to create a more user-friendly and understandable
system for patients compared to conventional CAD systems. In the future, LLM's
medical knowledge can be also used to improve the performance of vision-based
medical-image CAD models
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