# Traditional machine learning in biomedical image analysis: before you go too deep

## TL;DR

This paper reviews traditional machine learning's role in biomedical imaging, emphasizing its strengths in interpretability and efficiency compared to deep learning.

## Contribution

The paper provides a comprehensive review of traditional machine learning applications in biomedical imaging, emphasizing their continued relevance.

## Key findings

- Traditional machine learning excels in multimodal data integration and interpretability.
- TML is robust for smaller datasets and rapid prototyping in biomedical imaging.
- Democratized tools and clinical validation support TML's ongoing use alongside deep learning.

## Abstract

Traditional machine learning (TML) algorithms remain indispensable tools for the analysis of biomedical images, offering significant advantages in multimodal data integration, interpretability, computational efficiency, and robustness on smaller datasets. This review provides a comprehensive examination of TML applications across a broad spectrum of biomedical imaging modalities, highlighting its core principles, practical implementation, and unique benefits in the era of deep learning (DL). We outline the fundamental concepts of machine learning and describe key biomedical imaging tasks successfully addressed by TML. We also highlight the most popular platforms, which empower clinicians and researchers to utilize TML. DL now dominates many areas of medical image analysis due to superior performance and end-to-end feature learning. Using the most prominent examples, we analyze how TML retains unique value for applications with multimodal data processing, limited data, interpretability requirements, or rapid prototyping needs. Supported by increasingly democratized tools and validated by robust clinical studies, TML remains a vital methodology for extracting quantitative and qualitative insights from biomedical image data, ensuring its continued relevance in both research and clinical practice.