Cross Encoder-Decoder Transformer with Global-Local Visual Extractor for Medical Image Captioning

Transformer-based approaches have shown good results in image captioning tasks. However, current approaches have a limitation in generating text from global features of an entire image. Therefore, we propose novel methods for generating better image captioning as follows: (1) The Global-Local Visual...

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Vydané v:Sensors (Basel, Switzerland) Ročník 22; číslo 4; s. 1429
Hlavní autori: Lee, Hojun, Cho, Hyunjun, Park, Jieun, Chae, Jinyeong, Kim, Jihie
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland MDPI AG 01.02.2022
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deep learning
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Language processing
medical image captioning
Medical imaging equipment
Natural language interfaces
Neural networks
Noise
transformer
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transformer
deep learning
medical image captioning
ISSN:1424-8220, 1424-8220
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Abstract Transformer-based approaches have shown good results in image captioning tasks. However, current approaches have a limitation in generating text from global features of an entire image. Therefore, we propose novel methods for generating better image captioning as follows: (1) The Global-Local Visual Extractor (GLVE) to capture both global features and local features. (2) The Cross Encoder-Decoder Transformer (CEDT) for injecting multiple-level encoder features into the decoding process. GLVE extracts not only global visual features that can be obtained from an entire image, such as size of organ or bone structure, but also local visual features that can be generated from a local region, such as lesion area. Given an image, CEDT can create a detailed description of the overall features by injecting both low-level and high-level encoder outputs into the decoder. Each method contributes to performance improvement and generates a description such as organ size and bone structure. The proposed model was evaluated on the IU X-ray dataset and achieved better performance than the transformer-based baseline results, by 5.6% in BLEU score, by 0.56% in METEOR, and by 1.98% in ROUGE-L.
AbstractList Transformer-based approaches have shown good results in image captioning tasks. However, current approaches have a limitation in generating text from global features of an entire image. Therefore, we propose novel methods for generating better image captioning as follows: (1) The Global-Local Visual Extractor (GLVE) to capture both global features and local features. (2) The Cross Encoder-Decoder Transformer (CEDT) for injecting multiple-level encoder features into the decoding process. GLVE extracts not only global visual features that can be obtained from an entire image, such as size of organ or bone structure, but also local visual features that can be generated from a local region, such as lesion area. Given an image, CEDT can create a detailed description of the overall features by injecting both low-level and high-level encoder outputs into the decoder. Each method contributes to performance improvement and generates a description such as organ size and bone structure. The proposed model was evaluated on the IU X-ray dataset and achieved better performance than the transformer-based baseline results, by 5.6% in BLEU score, by 0.56% in METEOR, and by 1.98% in ROUGE-L.Transformer-based approaches have shown good results in image captioning tasks. However, current approaches have a limitation in generating text from global features of an entire image. Therefore, we propose novel methods for generating better image captioning as follows: (1) The Global-Local Visual Extractor (GLVE) to capture both global features and local features. (2) The Cross Encoder-Decoder Transformer (CEDT) for injecting multiple-level encoder features into the decoding process. GLVE extracts not only global visual features that can be obtained from an entire image, such as size of organ or bone structure, but also local visual features that can be generated from a local region, such as lesion area. Given an image, CEDT can create a detailed description of the overall features by injecting both low-level and high-level encoder outputs into the decoder. Each method contributes to performance improvement and generates a description such as organ size and bone structure. The proposed model was evaluated on the IU X-ray dataset and achieved better performance than the transformer-based baseline results, by 5.6% in BLEU score, by 0.56% in METEOR, and by 1.98% in ROUGE-L.
Transformer-based approaches have shown good results in image captioning tasks. However, current approaches have a limitation in generating text from global features of an entire image. Therefore, we propose novel methods for generating better image captioning as follows: (1) The Global-Local Visual Extractor (GLVE) to capture both global features and local features. (2) The Cross Encoder-Decoder Transformer (CEDT) for injecting multiple-level encoder features into the decoding process. GLVE extracts not only global visual features that can be obtained from an entire image, such as size of organ or bone structure, but also local visual features that can be generated from a local region, such as lesion area. Given an image, CEDT can create a detailed description of the overall features by injecting both low-level and high-level encoder outputs into the decoder. Each method contributes to performance improvement and generates a description such as organ size and bone structure. The proposed model was evaluated on the IU X-ray dataset and achieved better performance than the transformer-based baseline results, by 5.6% in BLEU score, by 0.56% in METEOR, and by 1.98% in ROUGE-L.
Audience Academic
Author Lee, Hojun
Chae, Jinyeong
Cho, Hyunjun
Park, Jieun
Kim, Jihie
AuthorAffiliation 2 Department of Artificial Intelligence, Dongguk University, Seoul 04620, Korea; jiny419@dgu.ac.kr
1 Department of Computer Science and Engineering, Dongguk University, Seoul 04620, Korea; cajun7@dgu.ac.kr (H.L.); chohyunjun1111@gmail.com (H.C.); 5656jieun@dgu.ac.kr (J.P.)
3 Okestro Ltd., Seoul 07326, Korea
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CitedBy_id crossref_primary_10_1038_s41598_025_00570_w
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Keywords transformer
deep learning
medical image captioning
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SubjectTerms Computational linguistics
Crop diseases
deep learning
Electric Power Supplies
Electric transformers
Evaluation
Language processing
medical image captioning
Medical imaging equipment
Natural language interfaces
Neural networks
Noise
transformer
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Title Cross Encoder-Decoder Transformer with Global-Local Visual Extractor for Medical Image Captioning
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