AI Medical Compendium Topic:
Semantics

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Improving broad-coverage medical entity linking with semantic type prediction and large-scale datasets.

Journal of biomedical informatics
OBJECTIVES: Biomedical natural language processing tools are increasingly being applied for broad-coverage information extraction-extracting medical information of all types in a scientific document or a clinical note. In such broad-coverage settings...

Visual-guided attentive attributes embedding for zero-shot learning.

Neural networks : the official journal of the International Neural Network Society
Zero-shot learning (ZSL) aims to learn a classifier for unseen classes by exploiting both training data from seen classes and external knowledge. In many visual tasks such as image classification, a set of high-level attributes that describe the sema...

Deep Learning on Construction Sites: A Case Study of Sparse Data Learning Techniques for Rebar Segmentation.

Sensors (Basel, Switzerland)
Recent advances in deep learning models for image interpretation finally made it possible to automate construction site monitoring processes that rely on remote sensing. However, the major drawback of these models is their dependency on large dataset...

A riddle, wrapped in a mystery, inside an enigma: How semantic black boxes and opaque artificial intelligence confuse medical decision-making.

Bioethics
The use of artificial intelligence (AI) in healthcare comes with opportunities but also numerous challenges. A specific challenge that remains underexplored is the lack of clear and distinct definitions of the concepts used in and/or produced by thes...

MR-Based Radiomics for Differential Diagnosis between Cystic Pituitary Adenoma and Rathke Cleft Cyst.

Computational and mathematical methods in medicine
BACKGROUND: It is often tricky to differentiate cystic pituitary adenoma from Rathke cleft cyst with visual inspection because of similar MRI presentations between them. We aimed to design an MR-based radiomics model for improving differential diagno...

A Fine-Tuned Bidirectional Encoder Representations From Transformers Model for Food Named-Entity Recognition: Algorithm Development and Validation.

Journal of medical Internet research
BACKGROUND: Recently, food science has been garnering a lot of attention. There are many open research questions on food interactions, as one of the main environmental factors, with other health-related entities such as diseases, treatments, and drug...

Toward a systematic conflict resolution framework for ontologies.

Journal of biomedical semantics
BACKGROUND: The ontology authoring step in ontology development involves having to make choices about what subject domain knowledge to include. This may concern sorting out ontological differences and making choices between conflicting axioms due to ...

Adversarial text-to-image synthesis: A review.

Neural networks : the official journal of the International Neural Network Society
With the advent of generative adversarial networks, synthesizing images from text descriptions has recently become an active research area. It is a flexible and intuitive way for conditional image generation with significant progress in the last year...

Explaining Black-Box Models for Biomedical Text Classification.

IEEE journal of biomedical and health informatics
In this paper, we propose a novel method named Biomedical Confident Itemsets Explanation (BioCIE), aiming at post-hoc explanation of black-box machine learning models for biomedical text classification. Using sources of domain knowledge and a confide...

Stopwords in technical language processing.

PloS one
There are increasing applications of natural language processing techniques for information retrieval, indexing, topic modelling and text classification in engineering contexts. A standard component of such tasks is the removal of stopwords, which ar...