14 research outputs found
ProSFDA: Prompt Learning based Source-free Domain Adaptation for Medical Image Segmentation
The domain discrepancy existed between medical images acquired in different
situations renders a major hurdle in deploying pre-trained medical image
segmentation models for clinical use. Since it is less possible to distribute
training data with the pre-trained model due to the huge data size and privacy
concern, source-free unsupervised domain adaptation (SFDA) has recently been
increasingly studied based on either pseudo labels or prior knowledge. However,
the image features and probability maps used by pseudo label-based SFDA and the
consistent prior assumption and the prior prediction network used by
prior-guided SFDA may become less reliable when the domain discrepancy is
large. In this paper, we propose a \textbf{Pro}mpt learning based \textbf{SFDA}
(\textbf{ProSFDA}) method for medical image segmentation, which aims to improve
the quality of domain adaption by minimizing explicitly the domain discrepancy.
Specifically, in the prompt learning stage, we estimate source-domain images
via adding a domain-aware prompt to target-domain images, then optimize the
prompt via minimizing the statistic alignment loss, and thereby prompt the
source model to generate reliable predictions on (altered) target-domain
images. In the feature alignment stage, we also align the features of
target-domain images and their styles-augmented counterparts to optimize the
source model, and hence push the model to extract compact features. We evaluate
our ProSFDA on two multi-domain medical image segmentation benchmarks. Our
results indicate that the proposed ProSFDA outperforms substantially other SFDA
methods and is even comparable to UDA methods. Code will be available at
\url{https://github.com/ShishuaiHu/ProSFDA}
Devil is in Channels: Contrastive Single Domain Generalization for Medical Image Segmentation
Deep learning-based medical image segmentation models suffer from performance
degradation when deployed to a new healthcare center. To address this issue,
unsupervised domain adaptation and multi-source domain generalization methods
have been proposed, which, however, are less favorable for clinical practice
due to the cost of acquiring target-domain data and the privacy concerns
associated with redistributing the data from multiple source domains. In this
paper, we propose a \textbf{C}hannel-level \textbf{C}ontrastive \textbf{S}ingle
\textbf{D}omain \textbf{G}eneralization (\textbf{CSDG}) model for medical
image segmentation. In CSDG, the shallower features of each image and its
style-augmented counterpart are extracted and used for contrastive training,
resulting in the disentangled style representations and structure
representations. The segmentation is performed based solely on the structure
representations. Our method is novel in the contrastive perspective that
enables channel-wise feature disentanglement using a single source domain. We
evaluated CSDG against six SDG methods on a multi-domain joint optic cup
and optic disc segmentation benchmark. Our results suggest the effectiveness of
each module in CSDG and also indicate that CSDG outperforms the
baseline and all competing methods with a large margin. The code will be
available at \url{https://github.com/ShishuaiHu/CCSDG}.Comment: 12 pages, 5 figure
Transformer-based Annotation Bias-aware Medical Image Segmentation
Manual medical image segmentation is subjective and suffers from
annotator-related bias, which can be mimicked or amplified by deep learning
methods. Recently, researchers have suggested that such bias is the combination
of the annotator preference and stochastic error, which are modeled by
convolution blocks located after decoder and pixel-wise independent Gaussian
distribution, respectively. It is unlikely that convolution blocks can
effectively model the varying degrees of preference at the full resolution
level. Additionally, the independent pixel-wise Gaussian distribution
disregards pixel correlations, leading to a discontinuous boundary. This paper
proposes a Transformer-based Annotation Bias-aware (TAB) medical image
segmentation model, which tackles the annotator-related bias via modeling
annotator preference and stochastic errors. TAB employs the Transformer with
learnable queries to extract the different preference-focused features. This
enables TAB to produce segmentation with various preferences simultaneously
using a single segmentation head. Moreover, TAB takes the multivariant normal
distribution assumption that models pixel correlations, and learns the
annotation distribution to disentangle the stochastic error. We evaluated our
TAB on an OD/OC segmentation benchmark annotated by six annotators. Our results
suggest that TAB outperforms existing medical image segmentation models which
take into account the annotator-related bias.Comment: 11 pages, 2 figure
Potential bioactive compounds and mechanisms of Fibraurea recisa Pierre for the treatment of Alzheimer’s disease analyzed by network pharmacology and molecular docking prediction
IntroductionHeat-clearing and detoxifying Chinese medicines have been documented to have anti-Alzheimer’s disease (AD) activities according to the accumulated clinical experience and pharmacological research results in recent decades. In this study, Fibraurea recisa Pierre (FRP), the classic type of Heat-clearing and detoxifying Chinese medicine, was selected as the object of research.Methods12 components with anti-AD activities were identified in FRP by a variety of methods, including silica gel column chromatography, multiple databases, and literature searches. Then, network pharmacology and molecular docking were adopted to systematically study the potential anti-AD mechanism of these compounds. Consequently, it was found that these 12 compounds could act on 235 anti-AD targets, of which AKT and other targets were the core targets. Meanwhile, among these 235 targets, 71 targets were identified to be significantly correlated with the pathology of amyloid beta (Aβ) and Tau.Results and discussionIn view of the analysis results of the network of active ingredients and targets, it was observed that palmatine, berberine, and other alkaloids in FRP were the key active ingredients for the treatment of AD. Further, Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis revealed that the neuroactive ligand-receptor interaction pathway and PI3K-Akt signaling pathway were the most significant signaling pathways for FRP to play an anti-AD role. Findings in our study suggest that multiple primary active ingredients in FRP can play a multitarget anti-AD effect by regulating key physiological processes such as neurotransmitter transmission and anti-inflammation. Besides, key ingredients such as palmatine and berberine in FRP are expected to be excellent leading compounds of multitarget anti-AD drugs
Highly Conductive, Anti-Freezing Hemicellulose-Based Hydrogels Prepared via Deep Eutectic Solvents and Their Applications
Hydrogels containing renewable resources, such as hemicellulose, have received a lot of attention owing to their softness and electrical conductivity which could be applied in soft devices and wearable equipment. However, traditional hemicellulose-based hydrogels generally exhibit poor electrical conductivity and suffer from freezing at lower temperatures owing to the presence of a lot of water. In this study, we dissolved hemicellulose by employing deep eutectic solvents (DESs), which were prepared by mixing choline chloride and imidazole. In addition, hemicellulose-based DES hydrogels were fabricated via photo-initiated reactions of acrylamide and hemicellulose with N, N′-Methylenebisacrylamide as a crosslinking agent. The produced hydrogels demonstrated high electrical conductivity and anti-freezing properties. The conductivity of the hydrogels was 2.13 S/m at room temperature and 1.97 S/m at −29 °C. The hydrogel’s freezing point was measured by differential scanning calorimetry (DSC) to be −47.78 °C. Furthermore, the hemicellulose-based DES hydrogels can function as a dependable and sensitive strain sensor for monitoring a variety of human activities
Data_Sheet_1_Potential bioactive compounds and mechanisms of Fibraurea recisa Pierre for the treatment of Alzheimer’s disease analyzed by network pharmacology and molecular docking prediction.PDF
IntroductionHeat-clearing and detoxifying Chinese medicines have been documented to have anti-Alzheimer’s disease (AD) activities according to the accumulated clinical experience and pharmacological research results in recent decades. In this study, Fibraurea recisa Pierre (FRP), the classic type of Heat-clearing and detoxifying Chinese medicine, was selected as the object of research.Methods12 components with anti-AD activities were identified in FRP by a variety of methods, including silica gel column chromatography, multiple databases, and literature searches. Then, network pharmacology and molecular docking were adopted to systematically study the potential anti-AD mechanism of these compounds. Consequently, it was found that these 12 compounds could act on 235 anti-AD targets, of which AKT and other targets were the core targets. Meanwhile, among these 235 targets, 71 targets were identified to be significantly correlated with the pathology of amyloid beta (Aβ) and Tau.Results and discussionIn view of the analysis results of the network of active ingredients and targets, it was observed that palmatine, berberine, and other alkaloids in FRP were the key active ingredients for the treatment of AD. Further, Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis revealed that the neuroactive ligand-receptor interaction pathway and PI3K-Akt signaling pathway were the most significant signaling pathways for FRP to play an anti-AD role. Findings in our study suggest that multiple primary active ingredients in FRP can play a multitarget anti-AD effect by regulating key physiological processes such as neurotransmitter transmission and anti-inflammation. Besides, key ingredients such as palmatine and berberine in FRP are expected to be excellent leading compounds of multitarget anti-AD drugs.</p
Table_1_Potential bioactive compounds and mechanisms of Fibraurea recisa Pierre for the treatment of Alzheimer’s disease analyzed by network pharmacology and molecular docking prediction.XLSX
IntroductionHeat-clearing and detoxifying Chinese medicines have been documented to have anti-Alzheimer’s disease (AD) activities according to the accumulated clinical experience and pharmacological research results in recent decades. In this study, Fibraurea recisa Pierre (FRP), the classic type of Heat-clearing and detoxifying Chinese medicine, was selected as the object of research.Methods12 components with anti-AD activities were identified in FRP by a variety of methods, including silica gel column chromatography, multiple databases, and literature searches. Then, network pharmacology and molecular docking were adopted to systematically study the potential anti-AD mechanism of these compounds. Consequently, it was found that these 12 compounds could act on 235 anti-AD targets, of which AKT and other targets were the core targets. Meanwhile, among these 235 targets, 71 targets were identified to be significantly correlated with the pathology of amyloid beta (Aβ) and Tau.Results and discussionIn view of the analysis results of the network of active ingredients and targets, it was observed that palmatine, berberine, and other alkaloids in FRP were the key active ingredients for the treatment of AD. Further, Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis revealed that the neuroactive ligand-receptor interaction pathway and PI3K-Akt signaling pathway were the most significant signaling pathways for FRP to play an anti-AD role. Findings in our study suggest that multiple primary active ingredients in FRP can play a multitarget anti-AD effect by regulating key physiological processes such as neurotransmitter transmission and anti-inflammation. Besides, key ingredients such as palmatine and berberine in FRP are expected to be excellent leading compounds of multitarget anti-AD drugs.</p