BioVFM-21M: Benchmarking and Scaling Self-Supervised Vision Foundation Models for Biomedical Image Analysis

TL;DR

This paper investigates how scaling model size, data, and algorithms affects biomedical image analysis, introduces a large dataset and a new foundation model, and demonstrates improved performance across multiple medical benchmarks.

Contribution

It introduces BioVFM-21M, a large-scale biomedical image dataset, and proposes a scalable medical vision foundation model trained on 21 million images, advancing understanding of scaling in medical AI.

Findings

Scaling benefits vary across tasks

Data diversity and task characteristics influence scaling effectiveness

BioVFM outperforms previous models on 12 benchmarks

Abstract

Scaling up model and data size have demonstrated impressive performance improvement over a wide range of tasks. Despite extensive studies on scaling behaviors for general-purpose tasks, medical images exhibit substantial differences from natural data. It remains unclear the key factors in developing medical vision foundation models at scale due to the absence of an extensive understanding of scaling behavior in the medical domain. In this paper, we explored the scaling behavior across model sizes, training algorithms, data sizes, and imaging modalities in developing scalable medical vision foundation models by self-supervised learning. To support scalable pretraining, we introduce BioVFM-21M, a large-scale biomedical image dataset encompassing a wide range of biomedical image modalities and anatomies. We observed that scaling up does provide benefits but varies across tasks. Additional analysis reveals several factors correlated with scaling benefits. Finally, we propose BioVFM, a large-scale medical vision foundation model pretrained on 21 million biomedical images, which outperforms the previous state-of-the-art foundation models across 12 medical benchmarks. Our results highlight that while scaling up is beneficial for pursuing better performance, task characteristics, data diversity, pretraining methods, and computational efficiency remain critical considerations for developing scalable medical foundation models.