Vision-Language Models for Acute Tuberculosis Diagnosis: A Multimodal Approach Combining Imaging and Clinical Data
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arXiv
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| Format: | Preprint |
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2025
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| author | Ganapthy, Ananya Shastry, Praveen Kumarasami, Naveen D, Anandakumar R, Keerthana M, Mounigasri M, Varshinipriya Venkatesh, Kishore Prasath Subramanian, Bargava Sivasailam, Kalyan |
| author_facet | Ganapthy, Ananya Shastry, Praveen Kumarasami, Naveen D, Anandakumar R, Keerthana M, Mounigasri M, Varshinipriya Venkatesh, Kishore Prasath Subramanian, Bargava Sivasailam, Kalyan |
| contents | Background: This study introduces a Vision-Language Model (VLM) leveraging SIGLIP and Gemma-3b architectures for automated acute tuberculosis (TB) screening. By integrating chest X-ray images and clinical notes, the model aims to enhance diagnostic accuracy and efficiency, particularly in resource-limited settings.
Methods: The VLM combines visual data from chest X-rays with clinical context to generate detailed, context-aware diagnostic reports. The architecture employs SIGLIP for visual encoding and Gemma-3b for decoding, ensuring effective representation of acute TB-specific pathologies and clinical insights.
Results: Key acute TB pathologies, including consolidation, cavities, and nodules, were detected with high precision (97percent) and recall (96percent). The model demonstrated strong spatial localization capabilities and robustness in distinguishing TB-positive cases, making it a reliable tool for acute TB diagnosis.
Conclusion: The multimodal capability of the VLM reduces reliance on radiologists, providing a scalable solution for acute TB screening. Future work will focus on improving the detection of subtle pathologies and addressing dataset biases to enhance its generalizability and application in diverse global healthcare settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_14538 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Vision-Language Models for Acute Tuberculosis Diagnosis: A Multimodal Approach Combining Imaging and Clinical Data Ganapthy, Ananya Shastry, Praveen Kumarasami, Naveen D, Anandakumar R, Keerthana M, Mounigasri M, Varshinipriya Venkatesh, Kishore Prasath Subramanian, Bargava Sivasailam, Kalyan Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning 68T07, 68T45, 92C55, 92C50, 68U10 Background: This study introduces a Vision-Language Model (VLM) leveraging SIGLIP and Gemma-3b architectures for automated acute tuberculosis (TB) screening. By integrating chest X-ray images and clinical notes, the model aims to enhance diagnostic accuracy and efficiency, particularly in resource-limited settings. Methods: The VLM combines visual data from chest X-rays with clinical context to generate detailed, context-aware diagnostic reports. The architecture employs SIGLIP for visual encoding and Gemma-3b for decoding, ensuring effective representation of acute TB-specific pathologies and clinical insights. Results: Key acute TB pathologies, including consolidation, cavities, and nodules, were detected with high precision (97percent) and recall (96percent). The model demonstrated strong spatial localization capabilities and robustness in distinguishing TB-positive cases, making it a reliable tool for acute TB diagnosis. Conclusion: The multimodal capability of the VLM reduces reliance on radiologists, providing a scalable solution for acute TB screening. Future work will focus on improving the detection of subtle pathologies and addressing dataset biases to enhance its generalizability and application in diverse global healthcare settings. |
| title | Vision-Language Models for Acute Tuberculosis Diagnosis: A Multimodal Approach Combining Imaging and Clinical Data |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning 68T07, 68T45, 92C55, 92C50, 68U10 |
| url | https://arxiv.org/abs/2503.14538 |