Automated Chest X-Ray Diagnosis Report Generation with Cross-Attention Mechanism
In the medical field, it is extremely important to use deep learning technology to automatically generate diagnostic reports for chest X-ray images. This technology provides an effective solution to the challenges faced by the medical field in processing large numbers of chest X-ray images. Especial...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/1/343 |
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| author | Jian Zhao Wei Yao Lei Sun Lijuan Shi Zhejun Kuang Changwu Wu Qiulei Han |
| author_facet | Jian Zhao Wei Yao Lei Sun Lijuan Shi Zhejun Kuang Changwu Wu Qiulei Han |
| author_sort | Jian Zhao |
| collection | DOAJ |
| description | In the medical field, it is extremely important to use deep learning technology to automatically generate diagnostic reports for chest X-ray images. This technology provides an effective solution to the challenges faced by the medical field in processing large numbers of chest X-ray images. Especially during large-scale outbreaks of epidemics such as the new COVID-19, rapid and accurate screening and diagnosis of cases become important tasks. This study uses deep learning technology to automatically generate diagnostic reports for chest X-ray images, which significantly reduces the workload of doctors, reduces the risk of misdiagnosis and missed diagnosis, and provides technical support for improving public health emergency response capabilities. In this study, we propose an innovative network architecture to address the limitations of traditional image description networks in generating chest X-ray diagnostic reports, especially the large area deviation between abnormal and normal areas, and the lack of effective alignment of the two modalities of image and text. The convolutional block attention module (CBAM) is adopted to effectively alleviate the data bias problem through a sophisticated feature attention mechanism and improve the model’s ability to recognize abnormal image areas. The cross-attention mechanism is adopted to optimize the alignment process between images and texts, ensuring the accuracy and reliability of the diagnosis report. |
| format | Article |
| id | doaj-art-13faba31c325465fa023623fac1680ea |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-13faba31c325465fa023623fac1680ea2025-01-10T13:15:13ZengMDPI AGApplied Sciences2076-34172025-01-0115134310.3390/app15010343Automated Chest X-Ray Diagnosis Report Generation with Cross-Attention MechanismJian Zhao0Wei Yao1Lei Sun2Lijuan Shi3Zhejun Kuang4Changwu Wu5Qiulei Han6College of Computer Science and Technology, Changchun University, Changchun 130022, ChinaCollege of Computer Science and Technology, Changchun University, Changchun 130022, ChinaCollege of Computer Science and Technology, Changchun University, Changchun 130022, ChinaJilin Provincial Key Laboratory of Human Health Status Identification Function & Enhancement, Changchun 130022, ChinaCollege of Computer Science and Technology, Changchun University, Changchun 130022, ChinaCollege of Computer Science and Technology, Changchun University, Changchun 130022, ChinaCollege of Computer Science and Technology, Changchun University, Changchun 130022, ChinaIn the medical field, it is extremely important to use deep learning technology to automatically generate diagnostic reports for chest X-ray images. This technology provides an effective solution to the challenges faced by the medical field in processing large numbers of chest X-ray images. Especially during large-scale outbreaks of epidemics such as the new COVID-19, rapid and accurate screening and diagnosis of cases become important tasks. This study uses deep learning technology to automatically generate diagnostic reports for chest X-ray images, which significantly reduces the workload of doctors, reduces the risk of misdiagnosis and missed diagnosis, and provides technical support for improving public health emergency response capabilities. In this study, we propose an innovative network architecture to address the limitations of traditional image description networks in generating chest X-ray diagnostic reports, especially the large area deviation between abnormal and normal areas, and the lack of effective alignment of the two modalities of image and text. The convolutional block attention module (CBAM) is adopted to effectively alleviate the data bias problem through a sophisticated feature attention mechanism and improve the model’s ability to recognize abnormal image areas. The cross-attention mechanism is adopted to optimize the alignment process between images and texts, ensuring the accuracy and reliability of the diagnosis report.https://www.mdpi.com/2076-3417/15/1/343deep learning technologychest X-ray imagesautomatic generation of diagnostic reportscross-attentionimage and text alignment |
| spellingShingle | Jian Zhao Wei Yao Lei Sun Lijuan Shi Zhejun Kuang Changwu Wu Qiulei Han Automated Chest X-Ray Diagnosis Report Generation with Cross-Attention Mechanism Applied Sciences deep learning technology chest X-ray images automatic generation of diagnostic reports cross-attention image and text alignment |
| title | Automated Chest X-Ray Diagnosis Report Generation with Cross-Attention Mechanism |
| title_full | Automated Chest X-Ray Diagnosis Report Generation with Cross-Attention Mechanism |
| title_fullStr | Automated Chest X-Ray Diagnosis Report Generation with Cross-Attention Mechanism |
| title_full_unstemmed | Automated Chest X-Ray Diagnosis Report Generation with Cross-Attention Mechanism |
| title_short | Automated Chest X-Ray Diagnosis Report Generation with Cross-Attention Mechanism |
| title_sort | automated chest x ray diagnosis report generation with cross attention mechanism |
| topic | deep learning technology chest X-ray images automatic generation of diagnostic reports cross-attention image and text alignment |
| url | https://www.mdpi.com/2076-3417/15/1/343 |
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