Cancer survival rate prediction using residual neural network on 3D non-spatial data
Improved cancer prognosis is an important goal of precision health medicine. Triple negative breast cancer (TNBC), being an aggressive form of cancer, requires novel and effective treatment. Deep Learning and its ability to model complex data inputs presents itself as a useful candidate for this app...
Saved in:
Main Author: | Chua, Yue Da |
---|---|
Other Authors: | Cai Yiyu |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2020
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/138613 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
3D spatial perception for underwater robots using point cloud data from orthogonal multibeam sonars fusion
by: Nicholas Sadjoli
Published: (2023) -
Prediction of penetration rate by GRU neural network
by: Leo, Kew Xun Wei
Published: (2022) -
Analysis of vehicle survival rates for Metro-Manila
by: Rith, Monorom, et al.
Published: (2019) -
Evaporation prediction of ethanol droplet by statistical rate theory
by: Mao, Wenjian
Published: (2019) -
3D printing of non-assembly mechanisms
by: Lai, David Yong Kuang
Published: (2022)