AIMC Topic: Data Curation

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Semi-Automated Data Curation from Biomedical Literature.

AMIA ... Annual Symposium proceedings. AMIA Symposium Apr 29, 2023
Data curation is a bottleneck for many informatics pipelines. A specific example of this is aggregating data from preclinical studies to identify novel genetic pathways for atherosclerosis in humans. This requires extracting data from published mouse...
Natural Language Processing Publications Data Curation Humans Animals Mice
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Clinicians' perception of oral potentially malignant disorders: a pitfall for image annotation in supervised learning.

Oral surgery, oral medicine, oral pathology and oral radiology Mar 7, 2023
OBJECTIVE: The present study aims to quantify clinicians' perceptions of oral potentially malignant disorders (OPMDs) when evaluating, classifying, and manually annotating clinical images, as well as to understand the source of inter-observer variabi...
Artificial Intelligence Supervised Machine Learning Humans Perception Data Curation Observer Variation Precancerous Conditions
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KaIDA: a modular tool for assisting image annotation in deep learning.

Journal of integrative bioinformatics Aug 26, 2022
Deep learning models achieve high-quality results in image processing. However, to robustly optimize parameters of deep neural networks, large annotated datasets are needed. Image annotation is often performed manually by experts without a comprehens...
Data Curation Neural Networks, Computer Deep Learning Image Processing, Computer-Assisted
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An Efficient Semi-Supervised Framework with Multi-Task and Curriculum Learning for Medical Image Segmentation.

International journal of neural systems Jul 30, 2022
A practical problem in supervised deep learning for medical image segmentation is the lack of labeled data which is expensive and time-consuming to acquire. In contrast, there is a considerable amount of unlabeled data available in the clinic. To mak...
Image Processing, Computer-Assisted Data Curation Curriculum Supervised Machine Learning Datasets as Topic
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Comparison of radiologist versus natural language processing-based image annotations for deep learning system for tuberculosis screening on chest radiographs.

Clinical imaging Apr 25, 2022
Although natural language processing (NLP) can rapidly extract disease labels from radiology reports to create datasets for deep learning models, this may be less accurate than having radiologists manually review the images. In this study, we compare...
Data Curation Deep Learning Natural Language Processing Radiologists Radiography, Thoracic Humans Retrospective Studies
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Self-Supervised Robust Feature Matching Pipeline for Teach and Repeat Navigation.

Sensors (Basel, Switzerland) Apr 7, 2022
The performance of deep neural networks and the low costs of computational hardware has made computer vision a popular choice in many robotic systems. An attractive feature of deep-learned methods is their ability to cope with appearance changes caus...
Neural Networks, Computer Research Design Data Curation Reproducibility of Results Hand
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Active label cleaning for improved dataset quality under resource constraints.

Nature communications Mar 4, 2022
Imperfections in data annotation, known as label noise, are detrimental to the training of machine learning models and have a confounding effect on the assessment of model performance. Nevertheless, employing experts to remove label noise by fully re...
Data Curation Machine Learning Diagnostic Imaging Delivery of Health Care Benchmarking
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The Challenge of Data Annotation in Deep Learning-A Case Study on Whole Plant Corn Silage.

Sensors (Basel, Switzerland) Feb 18, 2022
Recent advances in computer vision are primarily driven by the usage of deep learning, which is known to require large amounts of data, and creating datasets for this purpose is not a trivial task. Larger benchmark datasets often have detailed proces...
Deep Learning Data Curation Supervised Machine Learning Zea mays Silage
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The BMS-LM ontology for biomedical data reporting throughout the lifecycle of a research study: From data model to ontology.

Journal of biomedical informatics Feb 4, 2022
Biomedical research data reuse and sharing is essential for fostering research progress. To this aim, data producers need to master data management and reporting through standard and rich metadata, as encouraged by open data initiatives such as the F...
Research Design Biomedical Research Animals Metadata Biological Ontologies Semantics Data Curation
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Tracking cell lineages in 3D by incremental deep learning.

eLife Jan 6, 2022
Deep learning is emerging as a powerful approach for bioimage analysis. Its use in cell tracking is limited by the scarcity of annotated data for the training of deep-learning models. Moreover, annotation, training, prediction, and proofreading curre...
Image Processing, Computer-Assisted Humans Data Curation Deep Learning Cell Lineage Cell Tracking
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