DWARF: Disease-weighted network for attention map refinement
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916305520558080 |
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| author | Luo, Haozhe de Mortanges, Aurélie Pahud Inel, Oana Bernstein, Abraham Reyes, Mauricio |
| author_facet | Luo, Haozhe de Mortanges, Aurélie Pahud Inel, Oana Bernstein, Abraham Reyes, Mauricio |
| contents | The interpretability of deep learning is crucial for evaluating the reliability of medical imaging models and reducing the risks of inaccurate patient recommendations. This study addresses the "human out of the loop" and "trustworthiness" issues in medical image analysis by integrating medical professionals into the interpretability process. We propose a disease-weighted attention map refinement network (DWARF) that leverages expert feedback to enhance model relevance and accuracy. Our method employs cyclic training to iteratively improve diagnostic performance, generating precise and interpretable feature maps. Experimental results demonstrate significant improvements in interpretability and diagnostic accuracy across multiple medical imaging datasets. This approach fosters effective collaboration between AI systems and healthcare professionals, ultimately aiming to improve patient outcomes |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_17032 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DWARF: Disease-weighted network for attention map refinement Luo, Haozhe de Mortanges, Aurélie Pahud Inel, Oana Bernstein, Abraham Reyes, Mauricio Computer Vision and Pattern Recognition The interpretability of deep learning is crucial for evaluating the reliability of medical imaging models and reducing the risks of inaccurate patient recommendations. This study addresses the "human out of the loop" and "trustworthiness" issues in medical image analysis by integrating medical professionals into the interpretability process. We propose a disease-weighted attention map refinement network (DWARF) that leverages expert feedback to enhance model relevance and accuracy. Our method employs cyclic training to iteratively improve diagnostic performance, generating precise and interpretable feature maps. Experimental results demonstrate significant improvements in interpretability and diagnostic accuracy across multiple medical imaging datasets. This approach fosters effective collaboration between AI systems and healthcare professionals, ultimately aiming to improve patient outcomes |
| title | DWARF: Disease-weighted network for attention map refinement |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.17032 |