Enhanced Masked Image Modeling for Analysis of Dental Panoramic Radiographs (2306.10623v1)
Abstract: The computer-assisted radiologic informative report has received increasing research attention to facilitate diagnosis and treatment planning for dental care providers. However, manual interpretation of dental images is limited, expensive, and time-consuming. Another barrier in dental imaging is the limited number of available images for training, which is a challenge in the era of deep learning. This study proposes a novel self-distillation (SD) enhanced self-supervised learning on top of the masked image modeling (SimMIM) Transformer, called SD-SimMIM, to improve the outcome with a limited number of dental radiographs. In addition to the prediction loss on masked patches, SD-SimMIM computes the self-distillation loss on the visible patches. We apply SD-SimMIM on dental panoramic X-rays for teeth numbering, detection of dental restorations and orthodontic appliances, and instance segmentation tasks. Our results show that SD-SimMIM outperforms other self-supervised learning methods. Furthermore, we augment and improve the annotation of an existing dataset of panoramic X-rays.
- “Parameters of radiologic care: An official report of the american academy of oral and maxillofacial radiology,” Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontology, vol. 91, no. 5, pp. 498–511, 2001.
- VE Rushton and K Horner, “The use of panoramic radiology in dental practice,” Journal of dentistry, vol. 24, no. 3, pp. 185–201, 1996.
- DB Smith, “The numbering of teeth,” New Zealand School Dental Service gazette, vol. 37, no. 4, pp. 56, 1976.
- Björn Molander, “Panoramic radiography in dental diagnostics.,” Swedish Dental journal. Supplement, vol. 119, pp. 1–26, 1996.
- “Swin transformer: Hierarchical vision transformer using shifted windows,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 10012–10022.
- “Simmim: A simple framework for masked image modeling,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 9653–9663.
- “Masked autoencoders are scalable vision learners,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 16000–16009.
- “Uniform masking: Enabling mae pre-training for pyramid-based vision transformers with locality,” arXiv preprint arXiv:2205.10063, 2022.
- “An image is worth 16x16 words: Transformers for image recognition at scale,” ICLR, 2021.
- “Self-distillation augmented masked autoencoders for histopathological image classification,” arXiv preprint arXiv:2203.16983, 2022.
- “A study on tooth segmentation and numbering using end-to-end deep neural networks,” in 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). IEEE, 2020, pp. 164–171.
- “Self-supervised learning with masked image modeling for teeth numbering, detection of dental restorations, and instance segmentation in dental panoramic radiographs,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023, pp. 5594–5603.
- “Distilling the knowledge in a neural network,” NIPS Deep Learning and Representation Learning Workshop, 2014.
- “Be your own teacher: Improve the performance of convolutional neural networks via self distillation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019, pp. 3713–3722.
- Justin Brooks, “Coco annotator,” https://github.com/jsbroks/coco-annotator/, 2019.
- “Pytorch: An imperative style, high-performance deep learning library,” Advances in neural information processing systems, vol. 32, 2019.
- “Decoupled weight decay regularization,” ICLR, 2019.
- “Emerging properties in self-supervised vision transformers,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 9650–9660.
- Amani Almalki (3 papers)
- Longin Jan Latecki (25 papers)