Browse
Submit Data
Databases
API
Help

Dataset Information

25 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Context Matters: Graph-based Self-supervised Representation Learning for Medical Images.

ABSTRACT: Supervised learning method requires a large volume of annotated datasets. Collecting such datasets is time-consuming and expensive. Until now, very few annotated COVID-19 imaging datasets are available. Although self-supervised learning enables us to bootstrap the training by exploiting unlabeled data, the generic self-supervised methods for natural images do not sufficiently incorporate the context. For medical images, a desirable method should be sensitive enough to detect deviation from normal-appearing tissue of each anatomical region; here, anatomy is the context. We introduce a novel approach with two levels of self-supervised representation learning objectives: one on the regional anatomical level and another on the patient-level. We use graph neural networks to incorporate the relationship between different anatomical regions. The structure of the graph is informed by anatomical correspondences between each patient and an anatomical atlas. In addition, the graph representation has the advantage of handling any arbitrarily sized image in full resolution. Experiments on large-scale Computer Tomography (CT) datasets of lung images show that our approach compares favorably to baseline methods that do not account for the context. We use the learnt embedding to quantify the clinical progression of COVID-19 and show that our method generalizes well to COVID-19 patients from different hospitals. Qualitative results suggest that our model can identify clinically relevant regions in the images.

SUBMITTER: Sun L

PROVIDER: S-EPMC8513790 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Self-supervised graph representation learning integrates multiple molecular networks and decodes gene-disease relationships.

Project description:Leveraging molecular networks to discover disease-relevant modules is a long-standing challenge. With the accumulation of interactomes, there is a pressing need for powerful computational approaches to handle the inevitable noise and context-specific nature of biological networks. Here, we introduce Graphene, a two-step self-supervised representation learning framework tailored to concisely integrate multiple molecular networks and adapted to gene functional analysis via downstream re-training. In practice, we first leverage GNN (graph neural network) pre-training techniques to obtain initial node embeddings followed by re-training Graphene using a graph attention architecture, achieving superior performance over competing methods for pathway gene recovery, disease gene reprioritization, and comorbidity prediction. Graphene successfully recapitulates tissue-specific gene expression across disease spectrum and demonstrates shared heritability of common mental disorders. Graphene can be updated with new interactomes or other omics features. Graphene holds promise to decipher gene function under network context and refine GWAS (genome-wide association study) hits and offers mechanistic insights via decoding diseases from genome to networks to phenotypes.

| S-EPMC9868676 | biostudies-literature

Self-supervised global context graph neural network for session-based recommendation.

Project description:Session-based recommendation (SBR) aims to recommend the next items based on anonymous behavior sequences over a short period of time. Compared with other recommendation paradigms, the information available in SBR is very limited. Therefore, capturing the item relations across sessions is crucial for SBR. Recently, many methods have been proposed to learn article transformation relationships over all sessions. Despite their success, these methods may enlarge the impact of noisy interactions and ignore the complex high-order relationship between non-adjacent items. In this study, we propose a self-supervised global context graph neural network (SGC-GNN) to model high-order transition relations between items over all sessions by using virtual context vectors, each of which connects to all items in a given session and enables to collect and propagation information beyond adjacent items. Moreover, to improve the robustness of the proposed model, we devise a contrastive self-supervised learning (SSL) module as an auxiliary task to jointly learn more robust representations of the items in sessions and train the model to fulfill the SBR task. Experimental results on three benchmark datasets demonstrate the superiority of our model over the state-of-the-art (SOTA) methods and validate the effectiveness of context vectors and the self-supervised module.

| S-EPMC9454781 | biostudies-literature

Self-supervised learning for predicting transcriptomic groups on whole slides images in intrahepatic cholangiocarcinoma

Project description:Backgrounds & Aims: Transcriptomic classification of intrahepatic cholangiocarcinoma (iCCA) has been recently improved from two classes to five classes, associated with pathological features, targetable genetic alterations and survival. Despite its prognostic and therapeutic value, the classification is not routinely performed due to technical limits such as insufficient material or the cost of molecular analyses. Our aim was to predict iCCA transcriptomic classes on whole-slide digital histological images (WSI) using a self-supervised learning (SSL) model. Methods: Gene expression was investigated using RNA sequencing to class samples in the five transcriptomic classes. We then used a SSL method called GigaSSL. We first trained our model on a discovery set of 769 slides (biopsy and surgical samples, 246 patients) in a 5-fold cross-validation scheme. We then validated the model in a TCGA (n= 29) and a French external validation set (n=32). Results: Our model showed good performance for predicting each transcriptomic class in the discovery set (Area under the curve, AUC: 0.55-0.81), particularly for the Hepatic stem-like class (most frequent group, 37% of cases), with AUC 0.81. The model generalised well for the prediction of four on five of the transcriptomic classes into the two validation sets with AUCs ranging from 0.76 to 0.80 in the TCGA set and from 0.62 to 0.92 in the French external set. Conclusion: We have developed and validated a SSL based model able to predict iCCA transcriptomic classes on routine histological slides (biopsy and surgical samples), which could have an impact on the management of patients.

2023-11-01 | GSE244807 | GEO

Self-supervised representation learning for surgical activity recognition.

Project description:PurposeVirtual reality-based simulators have the potential to become an essential part of surgical education. To make full use of this potential, they must be able to automatically recognize activities performed by users and assess those. Since annotations of trajectories by human experts are expensive, there is a need for methods that can learn to recognize surgical activities in a data-efficient way.MethodsWe use self-supervised training of deep encoder-decoder architectures to learn representations of surgical trajectories from video data. These representations allow for semi-automatic extraction of features that capture information about semantically important events in the trajectories. Such features are processed as inputs of an unsupervised surgical activity recognition pipeline.ResultsOur experiments document that the performance of hidden semi-Markov models used for recognizing activities in a simulated myomectomy scenario benefits from using features extracted from representations learned while training a deep encoder-decoder network on the task of predicting the remaining surgery progress.ConclusionOur work is an important first step in the direction of making efficient use of features obtained from deep representation learning for surgical activity recognition in settings where only a small fraction of the existing data is annotated by human domain experts and where those annotations are potentially incomplete.

| S-EPMC8589823 | biostudies-literature

Hierarchical Molecular Graph Self-Supervised Learning for property prediction

Project description:Molecular graph representation learning has shown considerable strength in molecular analysis and drug discovery. Due to the difficulty of obtaining molecular property labels, pre-training models based on self-supervised learning has become increasingly popular in molecular representation learning. Notably, Graph Neural Networks (GNN) are employed as the backbones to encode implicit representations of molecules in most existing works. However, vanilla GNN encoders ignore chemical structural information and functions implied in molecular motifs, and obtaining the graph-level representation via the READOUT function hinders the interaction of graph and node representations. In this paper, we propose Hierarchical Molecular Graph Self-supervised Learning (HiMol), which introduces a pre-training framework to learn molecule representation for property prediction. First, we present a Hierarchical Molecular Graph Neural Network (HMGNN), which encodes motif structure and extracts node-motif-graph hierarchical molecular representations. Then, we introduce Multi-level Self-supervised Pre-training (MSP), in which corresponding multi-level generative and predictive tasks are designed as self-supervised signals of HiMol model. Finally, superior molecular property prediction results on both classification and regression tasks demonstrate the effectiveness of HiMol. Moreover, the visualization performance in the downstream dataset shows that the molecule representations learned by HiMol can capture chemical semantic information and properties. Graph Neural Networks are employed to encode molecular graph representations, but structural information and chemical functions are largely missing. Here, the authors develop hierarchical molecular graph self-supervised learning as a framework to learn molecule representation for property prediction.

| S-EPMC9938270 | biostudies-literature

Self-Supervised Learning of Graph Neural Networks: A Unified Review.

Project description:Deep models trained in supervised mode have achieved remarkable success on a variety of tasks. When labeled samples are limited, self-supervised learning (SSL) is emerging as a new paradigm for making use of large amounts of unlabeled samples. SSL has achieved promising performance on natural language and image learning tasks. Recently, there is a trend to extend such success to graph data using graph neural networks (GNNs). In this survey, we provide a unified review of different ways of training GNNs using SSL. Specifically, we categorize SSL methods into contrastive and predictive models. In either category, we provide a unified framework for methods as well as how these methods differ in each component under the framework. Our unified treatment of SSL methods for GNNs sheds light on the similarities and differences of various methods, setting the stage for developing new methods and algorithms. We also summarize different SSL settings and the corresponding datasets used in each setting. To facilitate methodological development and empirical comparison, we develop a standardized testbed for SSL in GNNs, including implementations of common baseline methods, datasets, and evaluation metrics.

| S-EPMC9902037 | biostudies-literature

Self-supervised learning of cell type specificity from immunohistochemical images.

Project description:MotivationAdvances in bioimaging now permit in situ proteomic characterization of cell-cell interactions in complex tissues, with important applications across a spectrum of biological problems from development to disease. These methods depend on selection of antibodies targeting proteins that are expressed specifically in particular cell types. Candidate marker proteins are often identified from single-cell transcriptomic data, with variable rates of success, in part due to divergence between expression levels of proteins and the genes that encode them. In principle, marker identification could be improved by using existing databases of immunohistochemistry for thousands of antibodies in human tissue, such as the Human Protein Atlas. However, these data lack detailed annotations of the types of cells in each image.ResultsWe develop a method to predict cell type specificity of protein markers from unlabeled images. We train a convolutional neural network with a self-supervised objective to generate embeddings of the images. Using non-linear dimensionality reduction, we observe that the model clusters images according to cell types and anatomical regions for which the stained proteins are specific. We then use estimates of cell type specificity derived from an independent single-cell transcriptomics dataset to train an image classifier, without requiring any human labelling of images. Our scheme demonstrates superior classification of known proteomic markers in kidney compared to selection via single-cell transcriptomics.Availability and implementationCode and trained model are available at www.github.com/murphy17/HPA-SimCLR.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC9235491 | biostudies-literature

A self-supervised domain-general learning framework for human ventral stream representation.

Project description:Anterior regions of the ventral visual stream encode substantial information about object categories. Are top-down category-level forces critical for arriving at this representation, or can this representation be formed purely through domain-general learning of natural image structure? Here we present a fully self-supervised model which learns to represent individual images, rather than categories, such that views of the same image are embedded nearby in a low-dimensional feature space, distinctly from other recently encountered views. We find that category information implicitly emerges in the local similarity structure of this feature space. Further, these models learn hierarchical features which capture the structure of brain responses across the human ventral visual stream, on par with category-supervised models. These results provide computational support for a domain-general framework guiding the formation of visual representation, where the proximate goal is not explicitly about category information, but is instead to learn unique, compressed descriptions of the visual world.

| S-EPMC8789817 | biostudies-literature

Accurate prediction of molecular targets using a self-supervised image representation learning framework.

Project description:The clinical efficacy and safety of a drug is determined by its molecular targets in the human proteome. However, proteome-wide evaluation of all compounds in human, or even animal models, is challenging. In this study, we present an unsupervised pre-training deep learning framework, termed ImageMol, from 8.5 million unlabeled drug-like molecules to predict molecular targets of candidate compounds. The ImageMol framework is designed to pretrain chemical representations from unlabeled molecular images based on local- and global-structural characteristics of molecules from pixels. We demonstrate high performance of ImageMol in evaluation of molecular properties (i.e., drug's metabolism, brain penetration and toxicity) and molecular target profiles (i.e., human immunodeficiency virus) across 10 benchmark datasets. ImageMol shows high accuracy in identifying anti-SARS-CoV-2 molecules across 13 high-throughput experimental datasets from the National Center for Advancing Translational Sciences (NCATS) and we re-prioritized candidate clinical 3CL inhibitors for potential treatment of COVID-19. In summary, ImageMol is an active self-supervised image processing-based strategy that offers a powerful toolbox for computational drug discovery in a variety of human diseases, including COVID-19.

| S-EPMC8996628 | biostudies-literature

Semi-Self-Supervised Learning for Semantic Segmentation in Images with Dense Patterns.

Project description:Deep learning has shown potential in domains with large-scale annotated datasets. However, manual annotation is expensive, time-consuming, and tedious. Pixel-level annotations are particularly costly for semantic segmentation in images with dense irregular patterns of object instances, such as in plant images. In this work, we propose a method for developing high-performing deep learning models for semantic segmentation of such images utilizing little manual annotation. As a use case, we focus on wheat head segmentation. We synthesize a computationally annotated dataset-using a few annotated images, a short unannotated video clip of a wheat field, and several video clips with no wheat-to train a customized U-Net model. Considering the distribution shift between the synthesized and real images, we apply three domain adaptation steps to gradually bridge the domain gap. Only using two annotated images, we achieved a Dice score of 0.89 on the internal test set. When further evaluated on a diverse external dataset collected from 18 different domains across five countries, this model achieved a Dice score of 0.73. To expose the model to images from different growth stages and environmental conditions, we incorporated two annotated images from each of the 18 domains to further fine-tune the model. This increased the Dice score to 0.91. The result highlights the utility of the proposed approach in the absence of large-annotated datasets. Although our use case is wheat head segmentation, the proposed approach can be extended to other segmentation tasks with similar characteristics of irregularly repeating patterns of object instances.

| S-EPMC10013790 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data