Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/136871
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
Type: | Conference paper |
Title: | Censor-Aware Semi-supervised Learning for Survival Time Prediction from Medical Images |
Author: | Hermoza, R. Maicas, G. Nascimento, J.C. Carneiro, G. |
Citation: | Lecture Notes in Artificial Intelligence, 2022 / Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (ed./s), vol.13437, pp.213-222 |
Publisher: | Springer |
Issue Date: | 2022 |
Series/Report no.: | Lecture Notes in Computer Science; 13437 |
ISBN: | 978-3-031-16448-4 |
ISSN: | 0302-9743 1611-3349 |
Conference Name: | International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) (18 Sep 2022 - 22 Sep 2022 : Singapore) |
Editor: | Wang, L. Dou, Q. Fletcher, P.T. Speidel, S. Li, S. |
Statement of Responsibility: | Renato Hermoza, Gabriel Maicas, Jacinto C. Nascimento, Gustavo Carneiro |
Abstract: | Survival time prediction from medical images is important for treatment planning, where accurate estimations can improve healthcare quality. One issue affecting the training of survival models is censored data. Most of the current survival prediction approaches are based on Cox models that can deal with censored data, but their application scope is limited because they output a hazard function instead of a survival time. On the other hand, methods that predict survival time usually ignore censored data, resulting in an under-utilization of the training set. In this work, we propose a new training method that predicts survival time using all censored and uncensored data. We propose to treat censored data as samples with a lower-bound time to death and estimate pseudo labels to semi-supervise a censor-aware survival time regressor. We evaluate our method on pathology and x-ray images from the TCGA-GM and NLST datasets. Our results establish the state-of-the-art survival prediction accuracy on both datasets. |
Keywords: | Censored data; Noisy labels; Pathological images; Chest x-rays; Semi-supervised learning; Survival time prediction |
Rights: | © 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG |
DOI: | 10.1007/978-3-031-16449-1_21 |
Grant ID: | http://purl.org/au-research/grants/arc/DP180103232 http://purl.org/au-research/grants/arc/FT190100525 |
Published version: | https://link.springer.com/chapter/10.1007/978-3-031-16449-1_21 |
Appears in Collections: | Computer Science publications |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.