SHIELD: A Self-supervised, Silicosis-focused Hierarchical Imaging Framework for Occupational Lung Disease Diagnosis

Akhter, Yasmeena; Ranjan, Rishabh; Singh, Richa; Vatsa, Mayank

doi:10.24963/ijcai.2025/1060

SHIELD: A Self-supervised, Silicosis-focused Hierarchical Imaging Framework for Occupational Lung Disease Diagnosis

Yasmeena Akhter, Rishabh Ranjan, Richa Singh, Mayank Vatsa

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. AI and Social Good. Pages 9538-9546.

https://doi.org/10.24963/ijcai.2025/1060

Keywords

Humans and AI: General
Multidisciplinary Topics and Applications: General
Computer Vision: General
Machine Learning: General

Abstract

Silicosis is an irreversible lung disease caused by silica dust exposure in industrial settings. Early detection is crucial, but automatic diagnostic methods are hindered by limited data availability. We propose SHIELD - a self-supervised, Silicosis-focused Hierarchical Imaging framework for early occupational Lung disease Diagnosis. Our method leverages a multi-resolution jigsaw puzzle pretext task on CXR images to extract and preserve features for lung region analysis. By employing a pyramidal strategy to generate pretrained models at various resolutions, followed by fine-tuning and a two-level ensembling across diverse deep learning architectures, SHIELD achieves enhanced diagnostic accuracy. We validate our approach on a publicly collected CXR dataset of 3044 samples from public health centers in India. SHIELD achieves 72% accuracy, demonstrating up to 20% improvement over baseline approaches. This work advances medical image analysis and supports UN Sustainable Development Goal 3 by providing cost-effective early screening in resource-limited settings.

Yasmeena Akhter, Rishabh Ranjan, Richa Singh, and Mayank Vatsa. 2025. SHIELD: A Self-supervised, Silicosis-focused Hierarchical Imaging Framework for Occupational Lung Disease Diagnosis. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 9538–9546. https://doi.org/10.24963/ijcai.2025/1060

@inproceedings{ijcai2025p1060,
  title     = {SHIELD: A Self-supervised, Silicosis-focused Hierarchical Imaging Framework for Occupational Lung Disease Diagnosis},
  author    = {Akhter, Yasmeena and Ranjan, Rishabh and Singh, Richa and Vatsa, Mayank},
  booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
               Artificial Intelligence, {IJCAI-25}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {James Kwok},
  pages     = {9538--9546},
  year      = {2025},
  month     = {8},
  note      = {AI and Social Good},
  doi       = {10.24963/ijcai.2025/1060},
  url       = {https://doi.org/10.24963/ijcai.2025/1060},
}

TY  - CPAPER
TI  - SHIELD: A Self-supervised, Silicosis-focused Hierarchical Imaging Framework for Occupational Lung Disease Diagnosis
AU  - Akhter, Yasmeena
AU  - Ranjan, Rishabh
AU  - Singh, Richa
AU  - Vatsa, Mayank
T2  - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2025
SP  - 9538
EP  - 9546
DO  - 10.24963/ijcai.2025/1060
UR  - https://doi.org/10.24963/ijcai.2025/1060
ER  -