Cross-Model Retrieval Via Automatic Medical Image Diagnosis Generation
| Autor: | Henda Ben Ghezala, Sabrine Benzarti, Wahiba Ben Abdessalem Karaa |
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| Rok vydání: | 2020 |
| Předmět: |
Closed captioning
Modalities Information retrieval Computer science business.industry Deep learning Image processing 02 engineering and technology Convolutional neural network Variety (cybernetics) Recurrent neural network 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Representation (mathematics) business |
| Zdroj: | Advances in Intelligent Systems and Computing ISBN: 9783030493417 ISDA |
| Popis: | Recent works in deep learning using Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) models have yielded state of the art results on a variety of image processing tasks. Multimodal representation, especially Image captioning is gaining popularity due to their primordial role in constricting heterogeneity gap among different modalities which are very helpful in cross-modality analysis tasks. The uncountable amounts of medical images, as well as medical documents, need to be processed to discover hidden knowledge. The purpose of this research is to present biomedical information retrieval system in order to know more about their strengths and weakness. Then we will propose our approach that tries to resolve some gaps and gives some solution to the existing systems and engine retrieval by giving an insight into the images captioning benefit in cross-modality retrieval. |
| Databáze: | OpenAIRE |
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