Paper Info
Title
TSDLPP: A Novel Two-Stage Deep Learning Framework For Prognosis Prediction Based on Whole Slide Histopathological Images
Abstract
Recently, digital pathology image-based prognosis prediction has become a hot topic in healthcare research to make early decisions on therapy and improve the treatment quality of patients. Therefore, there has been a recent surge of interest in designing deep learning method solving the problem of prognosis prediction with digital pathology images. However, whole slide histopathological images (WSIs) based prognosis prediction is still a challenge due to the large size of pathological images, the heterogeneity of tumors and the high cost of region of interests (ROIs) labeling. In this study, we design a novel two-stage deep learning framework for prognosis prediction (TSDLPP) based on WSIs. Our proposed framework consists of two-stage paradigms: 1) training tissue decomposition network (TDNet) to divide WSIs into cancerous and non-cancerous regions, 2) integrating general prognosis-related densely connected CNN (GPR-DCCNN) and morphology-specific prognosis-related densely connected CNNs (MSPR-DCCNNs) to extract different level features of pathological images. In the end, we apply TSDLPP to the prognosis prediction of breast cancer using The Cancer Genome Atlas (TCGA) datasets. Experiment results demonstrate that TSDLPP obtains superior performance of prognosis prediction compared with the existing state-of-arts methods.
Year
DOI
Venue
2022
10.1109/TCBB.2021.3080295
IEEE/ACM Transactions on Computational Biology and Bioinformatics
Keywords
DocType
Volume
Breast Neoplasms,Deep Learning,Female,Humans
Journal
19
Issue
ISSN
Citations 
4
1545-5963
0
PageRank 
References 
Authors
0.34
20
5
Name
Order
Citations
PageRank
Yu Liu149230.80
Ao Li221125.18
Jiangshu Liu300.34
Gang Meng400.34
Wang Minghui58524.90