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Foundation models in pathology
- Klauschen, Frederick;
- Dippel, Jonas;
- Mueller, Klaus-Robert
Citations
WEB OF SCIENCE
1Citations
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1초록
Foundation models prepare neural networks for applications in specific domains, such as speech applications or image analysis, through self-supervised pretraining. These models can be adapted for specific applications, such as histopathological diagnostics. While adaptation still requires supervised training, AI applications based on foundation models achieve significantly better prediction accuracy with fewer training data compared to conventional approaches. This article introduces the topic and provides an overview of foundation models in pathology.
키워드
Artificial intelligence; Computer-assisted image processing; Histopathology; Machine learning; Machine learning; Foundation models; Foundation models; Neural networks; Neural networks; Machine learning; Foundation models; Neural networks
- 제목
- Foundation models in pathology
- 저자
- Klauschen, Frederick; Dippel, Jonas; Mueller, Klaus-Robert
- 발행일
- 2025-05
- 유형
- Review
- 저널명
- Pathologie
- 권
- 46
- 호
- 3
- 페이지
- 152 ~ 155
- 언어
- GER
- 출판사
- SPRINGER HEIDELBERG
- 발행국가
- 독일
- 분량
- 4 페이지
- ISSN
- E 2731-7196
P 2731-7188