A systematic analysis of deep learning in genomics and histopathology for precision oncology
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 genomic data in cance...
Gespeichert in:
| Hauptverfasser: | , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
05 February 2024
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| In: |
BMC medical genomics
Year: 2024, Jahrgang: 17, Pages: 1-10 |
| ISSN: | 1755-8794 |
| DOI: | 10.1186/s12920-024-01796-9 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1186/s12920-024-01796-9
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| Verfasserangaben: | Michaela Unger and Jakob Nikolas Kather |
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| 520 | |a 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 genomic data in cancer. In addition, a number of recent studies have introduced multimodal DL models designed to simultaneously process both images from pathology slides and genomic data as inputs. By comparing patterns from one data modality with those in another, multimodal DL models are capable of achieving higher performance compared to their unimodal counterparts. However, the application of these methodologies across various tumor entities and clinical scenarios lacks consistency. | ||
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