An Attention Based Pipeline for Identifying Pre-Cancer Lesions in Head and Neck Clinical Images (2405.01937v2)
Abstract: Early detection of cancer can help improve patient prognosis by early intervention. Head and neck cancer is diagnosed in specialist centres after a surgical biopsy, however, there is a potential for these to be missed leading to delayed diagnosis. To overcome these challenges, we present an attention based pipeline that identifies suspected lesions, segments, and classifies them as non-dysplastic, dysplastic and cancerous lesions. We propose (a) a vision transformer based Mask R-CNN network for lesion detection and segmentation of clinical images, and (b) Multiple Instance Learning (MIL) based scheme for classification. Current results show that the segmentation model produces segmentation masks and bounding boxes with up to 82% overlap accuracy score on unseen external test data and surpassing reviewed segmentation benchmarks. Next, a classification F1-score of 85% on the internal cohort test set. An app has been developed to perform lesion segmentation taken via a smart device. Future work involves employing endoscopic video data for precise early detection and prognosis.
- T. T. Nevanpää et al., “Malignant transformation of oral epithelial dysplasia in Southwest Finland,” Scientific Reports, May 2022.
- “Head and neck cancers statistics,” 2019. [Online]. Available: https://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/head-and-neck-cancers
- M. Gormley et al., “Reviewing the epidemiology of head and neck cancer: definitions, trends and risk factors,” British Dental Journal, Nov. 2022.
- H. Mahmood, et al., “Prediction of malignant transformation and recurrence of oral epithelial dysplasia using architectural and cytological feature specific prognostic models,” Modern Pathology, Sep. 2022.
- S. Camalan et al., “Convolutional Neural Network-Based Clinical Predictors of Oral Dysplasia: Class Activation Map Analysis of Deep Learning Results,” Cancers, Mar. 2021.
- A. J. Shephard et al., “A Fully Automated and Explainable Algorithm for the Prediction of Malignant Transformation in Oral Epithelial Dysplasia,” arXiv preprint arXiv:2307.03757, 2023.
- H. Mahmood et al., “Artificial Intelligence-based methods in head and neck cancer diagnosis: an overview,” British Journal of Cancer, Jun. 2021.
- R. Anantharaman et al., “Utilizing Mask R-CNN for Detection and Segmentation of Oral Diseases,” in 2018 IEEE BIBM, Dec. 2018.
- Q. Fu et al., “A deep learning algorithm for detection of oral cavity squamous cell carcinoma from photographic images: A retrospective study,” EClinicalMedicine, Oct. 2020.
- K. He et al., “Mask R-CNN,” Jan. 2018, arXiv:1703.06870 [cs].
- S. Jadon, “A survey of loss functions for semantic segmentation,” in 2020 IEEE CIBCB, Oct. 2020.
- Y. Li et al., “Benchmarking Detection Transfer Learning with Vision Transformers,” Nov. 2021, arXiv:2111.11429 [cs].
- K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv:1409.1556, 2014.
- G. Huang et al., “Densely Connected Convolutional Networks,” arXiv:1608.06993, 2016.
- J. Deng et al., “ImageNet: A large-scale hierarchical image database,” in 2009 IEEE CVPR, Jun. 2009.
- G. Tanriver et al., “Automated Detection and Classification of Oral Lesions Using Deep Learning to Detect Oral Potentially Malignant Disorders,” Cancers, Jan. 2021.
- P. Achararit et al., “Artificial Intelligence-Based Diagnosis of Oral Lichen Planus Using Deep Convolutional Neural Networks,” European Journal of Dentistry, Jan. 2023.