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Updated: Sep 2, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Semi-supervised learning in cancer diagnostics.

Jan-Niklas Eckardt^1,2, Martin Bornhäuser^1,3,4, Karsten Wendt^2,5

¹Department of Internal Medicine I, University Hospital Carl Gustav Carus, Dresden, Germany.

Frontiers in Oncology

|August 1, 2022

Summary

Semi-supervised learning (SSL) effectively uses unlabeled data for cancer diagnostics, overcoming limitations of supervised learning (SL) which requires extensive manual labeling. This approach enhances cancer detection and clinical decision-making.

Keywords:

artificial intelligence cancer diagnostics machine learning semi-supervised learning

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Area of Science:

Oncology
Computer Science
Medical Diagnostics

Background:

Machine learning (ML) aids cancer diagnostics but supervised learning (SL) demands costly manual data labeling.
A significant gap exists between labeled and unlabeled data in cancer diagnostics.

Purpose of the Study:

To review semi-supervised learning (SSL) functionalities, assumptions, and applications in cancer diagnostics.
To highlight state-of-the-art SSL models in various oncology subfields.
To discuss challenges and future directions for SSL in oncology.

Main Methods:

Comprehensive literature review of SSL studies in cancer care.
Categorization of applications into image-based (histopathology, radiology, radiotherapy) and non-image-based (genomics).
Analysis of SSL methodologies and their assumptions.

Main Results:

SSL leverages unlabeled data to improve model performance, addressing the scarcity of labeled samples.
SSL models show promise across diverse areas including histopathology, radiology, radiotherapy, and genomics.
Identified potential pitfalls in SSL study design, such as data distribution discrepancies.

Conclusions:

Well-designed SSL models can significantly advance computer-guided cancer diagnostics.
SSL offers a viable solution to the challenge of sparse labeled and abundant unlabeled data in oncology.
SSL has the potential to overcome current data limitations in cancer diagnostics.