Weakly supervised temporal model for prediction of breast cancer distant recurrence
Abstract Efficient prediction of cancer recurrence in advance may help to recruit high risk breast cancer patients for clinical trial on-time and can guide a proper treatment plan. Several machine learning approaches have been developed for recurrence prediction in previous studies, but most of them...
Saved in:
Main Authors: | Josh Sanyal, Amara Tariq, Allison W. Kurian, Daniel Rubin, Imon Banerjee |
---|---|
Format: | article |
Language: | EN |
Published: |
Nature Portfolio
2021
|
Subjects: | |
Online Access: | https://doaj.org/article/621d9a9af23c4bb7b2dde427d5dfbe39 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Weak supervision as an efficient approach for automated seizure detection in electroencephalography
by: Khaled Saab, et al.
Published: (2020) -
AAWS-Net: Anatomy-aware weakly-supervised learning network for breast mass segmentation.
by: Yeheng Sun, et al.
Published: (2021) -
Breast Invasive Ductal Carcinoma Classification on Whole Slide Images with Weakly-Supervised and Transfer Learning
by: Fahdi Kanavati, et al.
Published: (2021) -
Methylation-to-Expression Feature Models of Breast Cancer Accurately Predict Overall Survival, Distant-Recurrence Free Survival, and Pathologic Complete Response in Multiple Cohorts
by: Jeffrey A. Thompson, et al.
Published: (2018) -
Factors affecting local recurrence and distant metastases of invasive breast cancer after breast-conserving surgery in Chiang Mai University Hospital
by: Ditsatham C, et al.
Published: (2016)