Back
#1
34.1%
Top 18%
9.9%
Top 4%
5.5%
Top 84%
4.6%
Top 2%
4.6%
Top 3%
4.2%
Top 10%
4.2%
Top 4%
4.2%
Top 3%
3.1%
Top 2%
2.1%
Top 3%
1.7%
Top 13%
1.5%
Top 4%
1.5%
Top 9%
1.1%
Top 4%
1.1%
Top 22%
1.0%
Top 5%
1.0%
Top 2%
0.8%
Top 4%
0.8%
Top 16%
0.8%
Top 9%
0.8%
Top 9%
0.8%
Top 9%
0.5%
Top 8%
0.5%
Machine learning enabled prediction of digital biomarkers from whole slide histopathology images
2024-01-08
oncology
Title + abstract only
View on medRxiv
Show abstract
Current predictive biomarkers generally leverage technologies such as immunohis-tochemistry or genetic analysis, which may require specialized equipment, be time-intensive to deploy, or incur human error. In this paper, we present an alternative approach for the development and deployment of a class of predictive biomarkers, leveraging deep learning on digital images of hematoxylin and eosin (H&E)-stained biopsy samples to simultaneously predict a range of molecular factors that are relevant to ...
Predicted journal destinations
1
Nature Communications
483 training papers
2
Scientific Reports
701 training papers
3
Cancers
57 training papers
4
PLOS ONE
1737 training papers
5
Clinical Cancer Research
22 training papers
6
Frontiers in Oncology
34 training papers
7
eLife
262 training papers
8
PLOS Computational Biology
141 training papers
9
Proceedings of the National Academy of Sciences
100 training papers
10
iScience
74 training papers
11
Cancer Medicine
17 training papers
12
npj Digital Medicine
85 training papers
13
British Journal of Cancer
22 training papers
14
Nature Genetics
72 training papers
15
Communications Medicine
63 training papers
16
JAMA Network Open
125 training papers
17
BMC Cancer
21 training papers
18
Cell Reports
25 training papers
19
Communications Biology
36 training papers
20
Nature Medicine
88 training papers
21
Computers in Biology and Medicine
39 training papers
22
Nature
58 training papers
23
Cell Reports Medicine
49 training papers
24
Journal of Clinical Investigation
50 training papers