Foundation X: Integrating Classification, Localization, and Segmentation through Lock-Release Pretraining Strategy for Chest X-ray Analysis

• URL: http://arxiv.org/abs/2503.09860v1
• Date: Wed, 12 Mar 2025 21:45:13 GMT
• Title: Foundation X: Integrating Classification, Localization, and Segmentation through Lock-Release Pretraining Strategy for Chest X-ray Analysis
• Authors: Nahid Ul Islam, DongAo Ma, Jiaxuan Pang, Shivasakthi Senthil Velan, Michael Gotway, Jianming Liang,
• Abstract summary: nFoundation X is an end-to-end framework that utilizes diverse expert-level annotations to train a foundation model.<n>We trained a model using 11 chest X-ray datasets, covering annotations for classification, localization, and segmentation tasks.<n>Our experimental results show that Foundation X achieves notable performance gains through extensive annotation utilization.
• Score: 3.874753046352665
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Developing robust and versatile deep-learning models is essential for enhancing diagnostic accuracy and guiding clinical interventions in medical imaging, but it requires a large amount of annotated data. The advancement of deep learning has facilitated the creation of numerous medical datasets with diverse expert-level annotations. Aggregating these datasets can maximize data utilization and address the inadequacy of labeled data. However, the heterogeneity of expert-level annotations across tasks such as classification, localization, and segmentation presents a significant challenge for learning from these datasets. To this end, we introduce nFoundation X, an end-to-end framework that utilizes diverse expert-level annotations from numerous public datasets to train a foundation model capable of multiple tasks including classification, localization, and segmentation. To address the challenges of annotation and task heterogeneity, we propose a Lock-Release pretraining strategy to enhance the cyclic learning from multiple datasets, combined with the student-teacher learning paradigm, ensuring the model retains general knowledge for all tasks while preventing overfitting to any single task. To demonstrate the effectiveness of Foundation X, we trained a model using 11 chest X-ray datasets, covering annotations for classification, localization, and segmentation tasks. Our experimental results show that Foundation X achieves notable performance gains through extensive annotation utilization, excels in cross-dataset and cross-task learning, and further enhances performance in organ localization and segmentation tasks. All code and pretrained models are publicly accessible at https://github.com/jlianglab/Foundation_X.

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