Zhao, A;
Shahin, AH;
Zhou, Y;
Gudmundsson, E;
Szmul, A;
Mogulkoc, N;
van Beek, F;
... Alexander, DC; + view all
(2022)
Prognostic Imaging Biomarker Discovery in Survival Analysis for Idiopathic Pulmonary Fibrosis.
In: Wang, L and Dou, Q and Fletcher, PT and Speidel, S and Li, S, (eds.)
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
(pp. pp. 223-233).
Springer: Cham, Switzerland.
Preview |
Text
MICCAI_submission_camera-ready.pdf - Accepted Version Download (3MB) | Preview |
Abstract
Imaging biomarkers derived from medical images play an important role in diagnosis, prognosis, and therapy response assessment. Developing prognostic imaging biomarkers which can achieve reliable survival prediction is essential for prognostication across various diseases and imaging modalities. In this work, we propose a method for discovering patch-level imaging patterns which we then use to predict mortality risk and identify prognostic biomarkers. Specifically, a contrastive learning model is first trained on patches to learn patch representations, followed by a clustering method to group similar underlying imaging patterns. The entire medical image can be thus represented by a long sequence of patch representations and their cluster assignments. Then a memory-efficient clustering Vision Transformer is proposed to aggregate all the patches to predict mortality risk of patients and identify high-risk patterns. To demonstrate the effectiveness and generalizability of our model, we test the survival prediction performance of our method on two sets of patients with idiopathic pulmonary fibrosis (IPF), a chronic, progressive, and life-threatening interstitial pneumonia of unknown etiology. Moreover, by comparing the high-risk imaging patterns extracted by our model with existing imaging patterns utilised in clinical practice, we can identify a novel biomarker that may help clinicians improve risk stratification of IPF patients.
| Type: | Proceedings paper |
|---|---|
| Title: | Prognostic Imaging Biomarker Discovery in Survival Analysis for Idiopathic Pulmonary Fibrosis |
| Event: | International Conference on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2022: |
| Location: | Singapore, SINGAPORE |
| Dates: | 18 Sep 2022 - 22 Sep 2022 |
| ISBN-13: | 9783031164484 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-031-16449-1_22 |
| Publisher version: | https://doi.org/10.1007/978-3-031-16449-1_22 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Technology, Life Sciences & Biomedicine, Computer Science, Interdisciplinary Applications, Imaging Science & Photographic Technology, Radiology, Nuclear Medicine & Medical Imaging, Computer Science, Imaging biomarker discovery, Survival analysis, Contrastive learning, Clustering vision transformer, Idiopathic pulmonary fibrosis |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine > Respiratory Medicine |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10160423 |
Loading...
Loading...Loading...| 1. |  United Kingdom | 13 |
| 2. |  United States | 13 |
| 3. |  Russian Federation | 3 |
| 4. |  France | 3 |
| 5. |  India | 2 |
| 6. |  China | 2 |
| 7. |  Korea, Republic of | 1 |
| 8. |  Iran, Islamic Republic of | 1 |
| 9. |  Spain | 1 |
| 10. |  Portugal | 1 |
Archive Staff Only
 |
View Item |
