# Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST

Source: [Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST](<https://www.sota2.com/research/paper/68fa76b71e209131cea6eb0d>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T04:00:18.171Z

Authors: Fuping Wu, Bartlomiej W. Papiez

Published: 2025-01-24T18:01:07.000Z

## Abstract

Foundation models are widely employed in medical image analysis, due to their high adaptability and generalizability for downstream tasks. With the increasing number of foundation models being released, model selection has become an important issue. In this work, we study the capabilities of foundation models in medical image classification tasks by conducting a benchmark study on the MedMNIST dataset. Specifically, we adopt various foundation models ranging from convolutional to Transformer-based models and implement both end-to-end training and linear probing for all classification tasks. The results demonstrate the significant potential of these pre-trained models when transferred for medical image classification. We further conduct experiments with different image sizes and various sizes of training data. By analyzing all the results, we provide preliminary, yet useful insights and conclusions on this topic.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2501.14685>)

[PDF](<https://arxiv.org/pdf/2501.14685.pdf>)