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Department of Information Technology

AI-Driven Large-scale Screening for Oral and Oropharyngeal Cancer

Funded by TDB Department of BioTechnology, Government of India
and VINNOVA Swedish Agency for Innovation Systems.


High mortality of oral and oropharyngeal cancer can be attributed to late diagnosis. Brush sampling and cytological analysis is efficient for early detection of cancer, but the analysis is costly and requires highly skilled expertise. Modern AI-techniques have made it feasible to radically reduce the analysis cost while at the same time increase speed and diagnostic accuracy. We have developed a deep learning based pipeline for efficient oral cancer screening and a low cost sample processing technique which meet the performance needs of large scale screening in resource-limited environments. The AIDOScan project aims to scale up our solutions towards large-scale implementation and usage in everyday healthcare in Sweden and India. The project will additionally explore if virus infections, such as HPV or COVID-19 can be detected using similar AI-cytology analysis.


  • N. Haj-Hosseini, J. Lindblad, B. Hasséus, V.V. Kumar, N. Subramaniam, J.-M. Hirsch. Early Detection of Oral Potentially Malignant Disorders: A Review on Prospective Screening Methods with Regard to Global Challenges. Journal of Maxillofacial and Oral Surgery. Published: 15 April 2022. doi:10.1007/s12663-022-01710-9, Open Access
  • J-M. Hirsch, N. Haj-Hosseini, C. Krüger Weiner, B. Hasséus, J. Lindblad. Icke-invasiv kontroll av cellförändringar i munslemhinnan (Non-invasive control of cell changes in the oral mucosa). Tandläkartidningen, Vol. 113, No. 9, pp. 48-55, 2021.
  • N. Koriakina, J. Lindblad, and N. Sladoje. The Effect of Within-Bag Sampling on End-to-End Multiple Instance Learning. In Proceedings of the 12th IEEE International Symposium on Image and Signal Processing and Analysis (ISPA), IEEE, pp. 183-188, Zagreb, Croatia, Sept. 2021. doi:10.1109/ISPA52656.2021.9552170
  • C. Rydell and J. Lindblad. CytoBrowser: a browser-based collaborative annotation platform for whole slide images. F1000Research 2021, 10:226. doi:10.12688/f1000research.51916.1, Open Access
  • K. Bengtsson Bernander, J. Lindblad, R. Strand, I. Nyström. Replacing data augmentation with rotation-equivariant CNNs in image-based classification of oral cancer. In Proceedings of the 25th Iberoamerican Congress on Pattern Recognition (CIARP), LNCS-12702, pp. 24-33, Porto, Portugal, May 2021. doi:10.1007/978-3-030-93420-0_3
  • E. Wetzer, J. Gay, H. Harlin, J. Lindblad, and N. Sladoje. When texture matters: Texture-focused CNNs outperform general data augmentation and pretraining in Oral Cancer Detection. In Proceedings of the 17th IEEE International Symposium on Biomedical Imaging (ISBI), IEEE, pp. 517-521, Iowa City, USA, April 2020. doi:ISBI45749.2020.9098424
  • J. Lu, N. Sladoje, C. Runow Stark, E. Darai Ramqvist, J-M. Hirsch, J. Lindblad. A Deep Learning based Pipeline for Efficient Oral Cancer Screening on Whole Slide Images. In Proceedings of the 17th International Conference on Image Analysis and Recognition (ICIAR), LNCS-12132, pp. 249-261, Póvoa de Varzim, Portugal, June 2020. doi:10.1007/978-3-030-50516-5_22

See also project page @ VINNOVA: 2020-03611

Updated  2022-08-14 00:02:18 by Joakim Lindblad.