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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2405.17459 |
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| _version_ | 1866916263320616960 |
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| author | Yao, Ziyan Lin, Fei Chai, Sheng He, Weijie Dai, Lu Fei, Xinghui |
| author_facet | Yao, Ziyan Lin, Fei Chai, Sheng He, Weijie Dai, Lu Fei, Xinghui |
| contents | In this paper, an innovative multi-modal deep learning model is proposed to deeply integrate heterogeneous information from medical images and clinical reports. First, for medical images, convolutional neural networks were used to extract high-dimensional features and capture key visual information such as focal details, texture and spatial distribution. Secondly, for clinical report text, a two-way long and short-term memory network combined with an attention mechanism is used for deep semantic understanding, and key statements related to the disease are accurately captured. The two features interact and integrate effectively through the designed multi-modal fusion layer to realize the joint representation learning of image and text. In the empirical study, we selected a large medical image database covering a variety of diseases, combined with corresponding clinical reports for model training and validation. The proposed multimodal deep learning model demonstrated substantial superiority in the realms of disease classification, lesion localization, and clinical description generation, as evidenced by the experimental results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_17459 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Integrating Medical Imaging and Clinical Reports Using Multimodal Deep Learning for Advanced Disease Analysis Yao, Ziyan Lin, Fei Chai, Sheng He, Weijie Dai, Lu Fei, Xinghui Machine Learning Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition In this paper, an innovative multi-modal deep learning model is proposed to deeply integrate heterogeneous information from medical images and clinical reports. First, for medical images, convolutional neural networks were used to extract high-dimensional features and capture key visual information such as focal details, texture and spatial distribution. Secondly, for clinical report text, a two-way long and short-term memory network combined with an attention mechanism is used for deep semantic understanding, and key statements related to the disease are accurately captured. The two features interact and integrate effectively through the designed multi-modal fusion layer to realize the joint representation learning of image and text. In the empirical study, we selected a large medical image database covering a variety of diseases, combined with corresponding clinical reports for model training and validation. The proposed multimodal deep learning model demonstrated substantial superiority in the realms of disease classification, lesion localization, and clinical description generation, as evidenced by the experimental results. |
| title | Integrating Medical Imaging and Clinical Reports Using Multimodal Deep Learning for Advanced Disease Analysis |
| topic | Machine Learning Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.17459 |