A systematic analysis of deep learning in genomics and histopathology for precision oncology
Abstract Background Digitized histopathological tissue slides and genomics profiling data are available for many patients with solid tumors. In the last 5 years, Deep Learning (DL) has been broadly used to extract clinically actionable information and biological knowledge from pathology slides and g...
Main Authors: | Michaela Unger, Jakob Nikolas Kather |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2024-02-01
|
Series: | BMC Medical Genomics |
Subjects: | |
Online Access: | https://doi.org/10.1186/s12920-024-01796-9 |
Similar Items
-
Deep learning in cancer genomics and histopathology
by: Michaela Unger, et al.
Published: (2024-03-01) -
Lung histopathological findings in COVID-19 disease – a systematic review
by: Giuseppe Pannone, et al.
Published: (2021-05-01) -
New-Onset and Relapsed Kidney Histopathology Following COVID-19 Vaccination: A Systematic Review
by: Henry H. L. Wu, et al.
Published: (2021-10-01) -
Deep Learning of Histopathological Features for the Prediction of Tumour Molecular Genetics
by: Pierre Murchan, et al.
Published: (2021-08-01) -
Deep Learning on Histopathological Images for Colorectal Cancer Diagnosis: A Systematic Review
by: Athena Davri, et al.
Published: (2022-03-01)