A Deep Network for Explainable Prediction of Non-Imaging Phenotypes using Anatomical Multi-View Data
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866911758105444352 |
|---|---|
| author | Wei, Yuxiang Chen, Yuqian Xue, Tengfei Zekelman, Leo Makris, Nikos Rathi, Yogesh Cai, Weidong Zhang, Fan Donnell, Lauren J. O' |
| author_facet | Wei, Yuxiang Chen, Yuqian Xue, Tengfei Zekelman, Leo Makris, Nikos Rathi, Yogesh Cai, Weidong Zhang, Fan Donnell, Lauren J. O' |
| contents | Large datasets often contain multiple distinct feature sets, or views, that offer complementary information that can be exploited by multi-view learning methods to improve results. We investigate anatomical multi-view data, where each brain anatomical structure is described with multiple feature sets. In particular, we focus on sets of white matter microstructure and connectivity features from diffusion MRI, as well as sets of gray matter area and thickness features from structural MRI. We investigate machine learning methodology that applies multi-view approaches to improve the prediction of non-imaging phenotypes, including demographics (age), motor (strength), and cognition (picture vocabulary). We present an explainable multi-view network (EMV-Net) that can use different anatomical views to improve prediction performance. In this network, each individual anatomical view is processed by a view-specific feature extractor and the extracted information from each view is fused using a learnable weight. This is followed by a wavelet transform-based module to obtain complementary information across views which is then applied to calibrate the view-specific information. Additionally, the calibrator produces an attention-based calibration score to indicate anatomical structures' importance for interpretation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_04579 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Deep Network for Explainable Prediction of Non-Imaging Phenotypes using Anatomical Multi-View Data Wei, Yuxiang Chen, Yuqian Xue, Tengfei Zekelman, Leo Makris, Nikos Rathi, Yogesh Cai, Weidong Zhang, Fan Donnell, Lauren J. O' Quantitative Methods Artificial Intelligence Image and Video Processing Large datasets often contain multiple distinct feature sets, or views, that offer complementary information that can be exploited by multi-view learning methods to improve results. We investigate anatomical multi-view data, where each brain anatomical structure is described with multiple feature sets. In particular, we focus on sets of white matter microstructure and connectivity features from diffusion MRI, as well as sets of gray matter area and thickness features from structural MRI. We investigate machine learning methodology that applies multi-view approaches to improve the prediction of non-imaging phenotypes, including demographics (age), motor (strength), and cognition (picture vocabulary). We present an explainable multi-view network (EMV-Net) that can use different anatomical views to improve prediction performance. In this network, each individual anatomical view is processed by a view-specific feature extractor and the extracted information from each view is fused using a learnable weight. This is followed by a wavelet transform-based module to obtain complementary information across views which is then applied to calibrate the view-specific information. Additionally, the calibrator produces an attention-based calibration score to indicate anatomical structures' importance for interpretation. |
| title | A Deep Network for Explainable Prediction of Non-Imaging Phenotypes using Anatomical Multi-View Data |
| topic | Quantitative Methods Artificial Intelligence Image and Video Processing |
| url | https://arxiv.org/abs/2401.04579 |