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arXiv · 2511.05600

Google-MedGemma Based Abnormality Detection in Musculoskeletal radiographs

Soumyajit Maity·Pranjal Kamboj·Sneha Maity·Rajat Singh·Sankhadeep Chatterjee
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Abstract

This paper proposes a MedGemma-based framework for automatic abnormality detection in musculoskeletal radiographs. Departing from conventional autoencoder and neural network pipelines, the proposed method leverages the MedGemma foundation model, incorporating a SigLIP-derived vision encoder pretrained on diverse medical imaging modalities. Preprocessed X-ray images are encoded into high-dimensional embeddings using the MedGemma vision backbone, which are subsequently passed through a lightweight multilayer perceptron for binary classification. Experimental assessment reveals that the MedGemma-driven classifier exhibits strong performance, exceeding conventional convolutional and autoencoder-based metrics. Additionally, the model leverages MedGemma's transfer learning capabilities, enhancing generalization and optimizing feature engineering. The integration of a modern medical foundation model not only enhances representation learning but also facilitates modular training strategies such as selective encoder block unfreezing for efficient domain adaptation. The findings suggest that MedGemma-powered classification systems can advance clinical radiograph triage by providing scalable and accurate abnormality detection, with potential for broader applications in automated medical image analysis. Keywords: Google MedGemma, MURA, Medical Image, Classification.

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Soumyajit Maity, Pranjal Kamboj, Sneha Maity, Rajat Singh, Sankhadeep Chatterjee. 2025-11-06. Google-MedGemma Based Abnormality Detection in Musculoskeletal radiographs. https://arxiv.org/abs/2511.05600

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