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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.01390 |
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| _version_ | 1866912902898778112 |
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| author | Caragliano, Alice Natalina Tacconi, Claudia Greco, Carlo Nibid, Lorenzo Ippolito, Edy Fiore, Michele Perrone, Giuseppe Ramella, Sara Soda, Paolo Guarrasi, Valerio |
| author_facet | Caragliano, Alice Natalina Tacconi, Claudia Greco, Carlo Nibid, Lorenzo Ippolito, Edy Fiore, Michele Perrone, Giuseppe Ramella, Sara Soda, Paolo Guarrasi, Valerio |
| contents | This study proposes a novel approach combining Multimodal Deep Learning with intrinsic eXplainable Artificial Intelligence techniques to predict pathological response in non-small cell lung cancer patients undergoing neoadjuvant therapy. Due to the limitations of existing radiomics and unimodal deep learning approaches, we introduce an intermediate fusion strategy that integrates imaging and clinical data, enabling efficient interaction between data modalities. The proposed Multimodal Doctor-in-the-Loop method further enhances clinical relevance by embedding clinicians' domain knowledge directly into the training process, guiding the model's focus gradually from broader lung regions to specific lesions. Results demonstrate improved predictive accuracy and explainability, providing insights into optimal data integration strategies for clinical applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_01390 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multimodal Doctor-in-the-Loop: A Clinically-Guided Explainable Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer Caragliano, Alice Natalina Tacconi, Claudia Greco, Carlo Nibid, Lorenzo Ippolito, Edy Fiore, Michele Perrone, Giuseppe Ramella, Sara Soda, Paolo Guarrasi, Valerio Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning This study proposes a novel approach combining Multimodal Deep Learning with intrinsic eXplainable Artificial Intelligence techniques to predict pathological response in non-small cell lung cancer patients undergoing neoadjuvant therapy. Due to the limitations of existing radiomics and unimodal deep learning approaches, we introduce an intermediate fusion strategy that integrates imaging and clinical data, enabling efficient interaction between data modalities. The proposed Multimodal Doctor-in-the-Loop method further enhances clinical relevance by embedding clinicians' domain knowledge directly into the training process, guiding the model's focus gradually from broader lung regions to specific lesions. Results demonstrate improved predictive accuracy and explainability, providing insights into optimal data integration strategies for clinical applications. |
| title | Multimodal Doctor-in-the-Loop: A Clinically-Guided Explainable Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2505.01390 |