Streamlining tuberculosis detection with foundation model-based weakly supervised transformer

Tuberculosis (TB) remains a major global health challenge, particularly in low- and middle-income countries. Traditional microscopy-based diagnostics are labor-intensive and error-prone, while automated deep learning models often require detailed expert annotations and intensive preprocessing, limiting their scalability. To address these challenges, we propose a weakly supervised approach for detecting Mycobacterium tuberculosis (MTB) in microscopy images, leveraging UNI, a foundation model pretrained on millions of pathology images. Our method encodes microscopy images as sequences of patch-level embeddings using UNI and applies a Transformer encoder to classify each image using only image-level labels, without requiring detailed annotations. This framework minimizes preprocessing, reduces annotation costs, and enhances scalability. Our model was trained and tested on large, diverse datasets, achieving high PR-AUC scores (0.943-0.974), demonstrating strong performance and robustness. This success highlights the potential of our approach, which introduces two key innovations not previously explored for automated TB detection: leveraging cross-domain transfer learning by applying UNI for MTB detection and using a weakly supervised approach that relies only on image-level labels, significantly reducing the annotation burden compared to traditional fully supervised methods. Our results underscore the feasibility of foundation models in TB diagnostics and broader medical imaging applications. This scalable, weakly supervised approach demonstrates promising experimental results, highlighting its potential to significantly reduce annotation requirements and streamline TB detection workflows, particularly relevant to resource-limited settings.

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