178 research outputs found
Semi-supervised Instance Segmentation with a Learned Shape Prior
To date, most instance segmentation approaches are based on supervised
learning that requires a considerable amount of annotated object contours as
training ground truth. Here, we propose a framework that searches for the
target object based on a shape prior. The shape prior model is learned with a
variational autoencoder that requires only a very limited amount of training
data: In our experiments, a few dozens of object shape patches from the target
dataset, as well as purely synthetic shapes, were sufficient to achieve results
en par with supervised methods with full access to training data on two out of
three cell segmentation datasets. Our method with a synthetic shape prior was
superior to pre-trained supervised models with access to limited
domain-specific training data on all three datasets. Since the learning of
prior models requires shape patches, whether real or synthetic data, we call
this framework semi-supervised learning
Stage-by-stage Wavelet Optimization Refinement Diffusion Model for Sparse-View CT Reconstruction
Diffusion models have emerged as potential tools to tackle the challenge of
sparse-view CT reconstruction, displaying superior performance compared to
conventional methods. Nevertheless, these prevailing diffusion models
predominantly focus on the sinogram or image domains, which can lead to
instability during model training, potentially culminating in convergence
towards local minimal solutions. The wavelet trans-form serves to disentangle
image contents and features into distinct frequency-component bands at varying
scales, adeptly capturing diverse directional structures. Employing the Wavelet
transform as a guiding sparsity prior significantly enhances the robustness of
diffusion models. In this study, we present an innovative approach named the
Stage-by-stage Wavelet Optimization Refinement Diffusion (SWORD) model for
sparse-view CT reconstruction. Specifically, we establish a unified
mathematical model integrating low-frequency and high-frequency generative
models, achieving the solution with optimization procedure. Furthermore, we
perform the low-frequency and high-frequency generative models on wavelet's
decomposed components rather than sinogram or image domains, ensuring the
stability of model training. Our method rooted in established optimization
theory, comprising three distinct stages, including low-frequency generation,
high-frequency refinement and domain transform. Our experimental results
demonstrate that the proposed method outperforms existing state-of-the-art
methods both quantitatively and qualitatively
Prediction and Characterization of Missing Proteomic Data in Desulfovibrio vulgaris
Proteomic datasets are often incomplete due to identification range and sensitivity issues. It becomes important to develop methodologies to estimate missing proteomic data, allowing better interpretation of proteomic datasets and metabolic mechanisms underlying complex biological systems. In this study, we applied an artificial neural network to approximate the relationships between cognate transcriptomic and proteomic datasets of Desulfovibrio vulgaris, and to predict protein abundance for the proteins not experimentally detected, based on several relevant predictors, such as mRNA abundance, cellular role and triple codon counts. The results showed that the coefficients of determination for the trained neural network models ranged from 0.47 to 0.68, providing better modeling than several previous regression models. The validity of the trained neural network model was evaluated using biological information (i.e. operons). To seek understanding of mechanisms causing missing proteomic data, we used a multivariate logistic regression analysis and the result suggested that some key factors, such as protein instability index, aliphatic index, mRNA abundance, effective number of codons (Nc) and codon adaptation index (CAI) values may be ascribed to whether a given expressed protein can be detected. In addition, we demonstrated that biological interpretation can be improved by use of imputed proteomic datasets
Two-and-a-half Order Score-based Model for Solving 3D Ill-posed Inverse Problems
Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are crucial
technologies in the field of medical imaging. Score-based models have proven to
be effective in addressing different inverse problems encountered in CT and
MRI, such as sparse-view CT and fast MRI reconstruction. However, these models
face challenges in achieving accurate three dimensional (3D) volumetric
reconstruction. The existing score-based models primarily focus on
reconstructing two dimensional (2D) data distribution, leading to
inconsistencies between adjacent slices in the reconstructed 3D volumetric
images. To overcome this limitation, we propose a novel two-and-a-half order
score-based model (TOSM). During the training phase, our TOSM learns data
distributions in 2D space, which reduces the complexity of training compared to
directly working on 3D volumes. However, in the reconstruction phase, the TOSM
updates the data distribution in 3D space, utilizing complementary scores along
three directions (sagittal, coronal, and transaxial) to achieve a more precise
reconstruction. The development of TOSM is built on robust theoretical
principles, ensuring its reliability and efficacy. Through extensive
experimentation on large-scale sparse-view CT and fast MRI datasets, our method
demonstrates remarkable advancements and attains state-of-the-art results in
solving 3D ill-posed inverse problems. Notably, the proposed TOSM effectively
addresses the inter-slice inconsistency issue, resulting in high-quality 3D
volumetric reconstruction.Comment: 10 pages, 13 figure
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