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Data Curation

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Visualizing Clinical Data Retrieval and Curation in Multimodal Healthcare AI Research: A Technical Note on RIL-workflow.

Journal of imaging informatics in medicine
Curating and integrating data from sources are bottlenecks to procuring robust training datasets for artificial intelligence (AI) models in healthcare. While numerous applications can process discrete types of clinical data, it is still time-consumin...

Deep learning-based image annotation for leukocyte segmentation and classification of blood cell morphology.

BMC medical imaging
The research focuses on the segmentation and classification of leukocytes, a crucial task in medical image analysis for diagnosing various diseases. The leukocyte dataset comprises four classes of images such as monocytes, lymphocytes, eosinophils, a...

On-the-fly point annotation for fast medical video labeling.

International journal of computer assisted radiology and surgery
PURPOSE: In medical research, deep learning models rely on high-quality annotated data, a process often laborious and time-consuming. This is particularly true for detection tasks where bounding box annotations are required. The need to adjust two co...

A Guideline for Open-Source Tools to Make Medical Imaging Data Ready for Artificial Intelligence Applications: A Society of Imaging Informatics in Medicine (SIIM) Survey.

Journal of imaging informatics in medicine
In recent years, the role of Artificial Intelligence (AI) in medical imaging has become increasingly prominent, with the majority of AI applications approved by the FDA being in imaging and radiology in 2023. The surge in AI model development to tack...

DUVEL: an active-learning annotated biomedical corpus for the recognition of oligogenic combinations.

Database : the journal of biological databases and curation
While biomedical relation extraction (bioRE) datasets have been instrumental in the development of methods to support biocuration of single variants from texts, no datasets are currently available for the extraction of digenic or even oligogenic vari...

Pseudo-class part prototype networks for interpretable breast cancer classification.

Scientific reports
Interpretability in machine learning has become increasingly important as machine learning is being used in more and more applications, including those with high-stakes consequences such as healthcare where Interpretability has been regarded as a key...

Annotation of epilepsy clinic letters for natural language processing.

Journal of biomedical semantics
BACKGROUND: Natural language processing (NLP) is increasingly being used to extract structured information from unstructured text to assist clinical decision-making and aid healthcare research. The availability of expert-annotated documents for the d...

SeqImprove: Machine-Learning-Assisted Curation of Genetic Circuit Sequence Information.

ACS synthetic biology
The progress and utility of synthetic biology is currently hindered by the lengthy process of studying literature and replicating poorly documented work. Reconstruction of crucial design information through post hoc curation is highly noisy and error...

Automated Workflows for Data Curation and Machine Learning to Develop Quantitative Structure-Activity Relationships.

Methods in molecular biology (Clifton, N.J.)
The recent advancements in machine learning and the new availability of large chemical datasets made the development of tools and protocols for computational chemistry a topic of high interest. In this chapter a standard procedure to develop Quantita...

Partial Annotation Learning for Biomedical Entity Recognition.

IEEE journal of biomedical and health informatics
Named Entity Recognition (NER) is a key task to support biomedical research. In Biomedical Named Entity Recognition (BioNER), obtaining high-quality expert annotated data is laborious and expensive, leading to the development of automatic approaches ...