Welikala, Roshan Alex and Remagnino, Paolo and Lim, Jian Han and Chan, Chee Seng and Rajendran, Senthilmani and Kallarakkal, Thomas George and Mohd Zain, Rosnah and Jayasinghe, Ruwan Duminda and Rimal, Jyotsna and Kerr, Alexander Ross and Amtha, Rahmi and Patil, Karthikeya and Tilakaratne, Wanninayake Mudiyanselage and Cheong, Sok Ching and Barman, Sarah Ann (2021) Clinically guided trainable soft attention for early detection of oral cancer. In: 19th International Conference on Computer Analysis of Images and Patterns, CAIP 2021, 28 - 30 September 2021, Virtual, Online.
Full text not available from this repository.Abstract
Oral cancer disproportionately affects low- and middle-income countries, where a lack of access to appropriate medical care contributes towards late disease presentation. Using artificial intelligence to facilitate the automated identification of high-risk oral lesions can improve patient survival rates. With image classification using oral cavity images and other forms of medical images, the information to be classified can often be extremely localized. To address this problem, we propose the use of convolutional neural networks with trainable soft attention. Further to this, we incorporate the use of localization loss to penalize the difference between attention maps and clinically annotated mask. This effectively allows clinicians to help guide soft attention. Improvements to the baseline were made, with an accuracy of 0.8333 and a ROC AUC of 0.8632, which equates to increases of 0.0245 and 0.0394, respectively. This accuracy corresponds to a sensitivity of 0.8469 and a specificity of 0.8208. Perhaps of more importance, is a model that demonstrates better capability at paying attention to the lesions in its decision making. Furthermore, visualizing resulting attention maps can help to strengthen clinical confidence in AI decision making. © 2021, Springer Nature Switzerland AG.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Funders: | Medical Research Council [Grant No: MR/S013865/1] |
| Uncontrolled Keywords: | Attention; Deep learning; Oral cancer; Oral potentially malignant disorders |
| Subjects: | R Medicine > RK Dentistry |
| Divisions: | Faculty of Dentistry |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 24 Oct 2023 06:22 |
| Last Modified: | 24 Oct 2023 06:23 |
| URI: | http://eprints.um.edu.my/id/eprint/35582 |
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