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Medical Concept Normalization
Medical concept normalization, which maps clinical entities to concepts in standard terminology, is essential for supporting downstream computational...
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Fast medical concept normalization for biomedical literature based on stack and index optimized self-attention
Medical concept normalization aims to construct a semantic map** between mentions and concepts and to uniformly represent mentions that belong to...
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Enhancing Cross-lingual Biomedical Concept Normalization Using Deep Neural Network Pretrained Language Models
In this study, we propose a new approach for cross-lingual biomedical concept normalization, the process of map** text in non-English documents to...
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Leveraging Wikipedia Knowledge for Distant Supervision in Medical Concept Normalization
The majority of recent research has approached the Medical Concept Normalization (MCN) task as supervised text classification. However, combining all...
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ClinLinker: Medical Entity Linking of Clinical Concept Mentions in Spanish
Advances in natural language processing techniques, such as named entity recognition and normalization to widely used standardized terminologies like...
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CateNorm: Categorical Normalization for Robust Medical Image Segmentation
Batch normalization (BN) uniformly shifts and scales the activations based on the statistics of a batch of images. However, the intensity...
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Attention adaptive instance normalization style transfer for vascular segmentation using deep learning
AbstractDeep learning models have demonstrated substantial progress in medical image segmentation. However, these models require large datasets for...
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Edge Weight Updating Neural Network for Named Entity Normalization
Discriminating the matched named entity pairs or identifying the entities’ canonical forms are critical in text mining tasks. More precise named...
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An in-place ABN-based denoiser for medical images
Noise in medical images can degrade the quality of the image and make it difficult to interpret. Denoising algorithms have been commonly used to...
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The Research of Medical Metaverse Application Under the Background of the Normalization of the New Crown Epidemic
At present, the traditional medical model is facing the prominent contradiction of the imbalance between supply and demand, and under the new...
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Biomedical Entity Normalization Using Encoder Regularization and Dynamic Ranking Mechanism
Biomedical entity normalization is a fundamental method for lots of downstream applications. Due to the rich additional information for biomedical...
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Evaluation of sparsity metrics and evolutionary algorithms applied for normalization of H&E histological images
Color variations in H&E histological images can impact the segmentation and classification stages of computational systems used for cancer diagnosis....
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Continuous Prompt Enhanced Biomedical Entity Normalization
Biomedical entity normalization (BEN) aims to link the entity mentions in a biomedical text to referent entities in a knowledge base. Recently, the...
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Evaluation of a Concept Map** Task Using Named Entity Recognition and Normalization in Unstructured Clinical Text
In this pilot study, we explore the feasibility and accuracy of using a query in a commercial natural language processing engine in a named entity...
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Content Based Medical Image Retrieval
Modern healthcare relies heavily on medical imaging, and as a result of its extensive use, image databases, picture archiving, and picture...
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Analysis of children's sub-health treatment effect based on multi-scale feature fusion network from the perspective of medical informatization
Sub-health state is a state of health and low quality between disease and health. The theoretical basis of children's sub-health is to start from the...
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Region-adaptive Concept Aggregation for Few-shot Visual Recognition
Few-shot learning (FSL) aims to learn novel concepts from very limited examples. However, most FSL methods suffer from the issue of lacking...
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SqueezeCapsNet: enhancing capsule networks with squeezenet for holistic medical and complex images
Early diagnosis of patients’ disease is crucial since it helps doctors and patients devise a treatment plan. Therefore, recognizing medical images...
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MedTSS: transforming abstractive summarization of scientific articles with linguistic analysis and concept reinforcement
This research addresses the limitations of pretrained models (PTMs) in generating accurate and comprehensive abstractive summaries for scientific...
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Visual Superordinate Abstraction for Robust Concept Learning
Concept learning constructs visual representations that are connected to linguistic semantics, which is fundamental to vision-language tasks....